Hello WOrld?

Selecting Best Performing Model

The table compares several different configurations of an LGBM model (based on tuning metric and included features).

	Model	auc	pr_auc	_f1_micro	_f1_macro	logloss	accuracy	precision_macro	recall_macro	f1_macro	target_f1	target_recall	target_precision	fbeta_1.5	fbeta_2.5	fbeta_4.0	log_loss	elapsed_time	total_size
0	LGBM_AUC	0.78	0.26	0.73	0.56	9.88	0.73	0.57	0.71	0.56	0.28	0.68	0.18	0.37	0.49	0.59	0.55	52.10	23.73
1	Baseline_Only_CreditRatings	0.73	0.20	0.67	0.52	11.77	0.67	0.56	0.67	0.52	0.25	0.67	0.15	0.32	0.45	0.56	0.61	2.10	2.29

We have selected LGBM_Dart_AUC as our final "production" model. LightGBM be default issues 'GBDT' (Gradient Boosting Decision Tree) as its boosting algorithm. 'DART' (Dropouts meet Multiple Additive Regression Trees ) is an alternativeboosting algorithm variant that helps prevent overfitting and improve model generalization by randomly dropping a fraction of boosting trees during training (similar to dropout in neural networks).

Because DART's process of dropping and adding trees back into the model requires more computational work and longer training periods, additionally the stochastic nature of the dropout process can lead to less consistent performance across different training runs compared to GBDT which might be problematic when tuning.

However, we have chose to use LGBM + DART as our primary model because it provides slightly better performance and it's theoretical advantages (reduce overfitting and better Generalization) outweigh the slower training time (5 fold CV + fitting a model on the full train set only take ~2 minutes).

V:\projects\ppuodz-ML.4.1\shared\graph.py:1057: UserWarning: The figure layout has changed to tight
  plt.tight_layout()

Overal we can see that while we are able relatively high AUC of 0.78-0.79 the classification performance, is still very poor. Specifically the very large rate of false positives is a concern because it would mean that ~32% of loans in the sample which were not defaulted on would be rejected when using our model.

On the other hand a somewhat large proportion of (~65%) of problematic loans were detected as such, considering the inherent risks of catering to clients with poorly know credit history that's a relative reasonable result.

We'll investigate this further but generally we can see that the model might be useful to Home Credit if they decided to take a more conservative approach to granting loans.

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\entityset\entityset.py:1914: UserWarning: index SK_BUREAU_ID not found in dataframe, creating new integer column
  warnings.warn(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\computational_backends\feature_set_calculator.py:828: FutureWarning: The provided callable <function max at 0x000001D1FF164400> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
  ).agg(to_agg)
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\computational_backends\feature_set_calculator.py:828: FutureWarning: The provided callable <function mean at 0x000001D1FF164E00> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
  ).agg(to_agg)
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\computational_backends\feature_set_calculator.py:828: FutureWarning: The provided callable <function min at 0x000001D1FF164540> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
  ).agg(to_agg)

Appending previous history
drop drop_cols_post_proc: 227
after drop_cols_post_proc: 121
Full DS size: 307511

pandas.core.series.Series

Feature Importance

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\shap\explainers\_tree.py:448: UserWarning: LightGBM binary classifier with TreeExplainer shap values output has changed to a list of ndarray
  warnings.warn('LightGBM binary classifier with TreeExplainer shap values output has changed to a list of ndarray')

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\shap\explainers\_tree.py:448: UserWarning: LightGBM binary classifier with TreeExplainer shap values output has changed to a list of ndarray
  warnings.warn('LightGBM binary classifier with TreeExplainer shap values output has changed to a list of ndarray')

Calculating interest rates:

To determine interest rates for each loan while accounting for default risk and aiming to maximize total return on a portfolio, we can employ several advanced modeling techniques. Determening appropriate interest rates gennerally involves not only predicting the risk of default but also integrating this risk assessment into pricing strategies that reflect the level of risk associated with each loan.

We'll use a simple Risk-Based Pricing Modelin our calculation: it directly links the interest rate charged on a loan to the estimated risk of default. The basic steps to build such a model are:

• Default Probability Estimation: We'll use the probability of default for each loan.
• Loss Given Default (LGD) and Expected Loss: is the amount of loss if the borrower defaults. This can be a fixed percentage or varied based on collateral or loan type (i.e. usually . The Expected Loss (EL) is then calculated as EL = PD × LGD
• where PD is the probability of default.
• We'll set the interest rate such that it covers the expected loss, plus a profit margin. For instance, if the cost of funds is 3%, the EL is 2%, and the desired profit margin is 2%, the minimum interest rate might be set at 7%.

Additionally more complex models could (and probably should) be used in real world scenarios that would incorporate the market conditions (i.e. base interbank rates, margins offered by competitors etc.).

2. Profit Maximization Model This model focuses on maximizing the total return by considering both the interest income and the potential losses due to defaults.

Optimization Framework: Use an optimization model that calculates the optimal interest rate for each loan type. This model would use inputs from the risk model (PD and LGD) and incorporate constraints like minimum return requirements, risk appetite, and regulatory requirements. Simulation Techniques: Simulate different interest rate scenarios for various risk levels to determine the interest rate that maximizes profit while keeping the default risk within acceptable bounds.

3. Portfolio Approach Instead of setting rates on a loan-by-loan basis, consider the entire portfolio's risk and return.

Portfolio Diversification: Assess the risk contribution of each loan type to the overall portfolio and adjust interest rates to achieve desired diversification and risk-return profile. Risk-adjusted Return on Capital (RAROC): Utilize RAROC to evaluate the profitability of a loan, considering the capital at risk. RAROC is used to ensure that the adjusted return (considering risk) meets a certain threshold.

Calibrating Probabilities

Mean. Model Predicted Probabilities = 39.92%

Actual Portfolio Default Rate = 8.07%

We can't directly use the probabilities output by our model to perform any financial analysis because they are not aligned with the true probabilities, which is necessary for risk-based pricing models.

We'll have to calibrate our probabilities using CalibratedClassifierCV and using Isotonic Regression (A non-parametric approach that provides a piecewise linear calibration)

Mean. Model Calibrated Probabilities = 8.35%

Calculating hypothetical interest rates

The application_test.csv does not contain data on the interest rate for granted loans which makes it impossible to calculate the "base" rate used by Home Credit. For our example we'll use a base interest rate of 4% (round 12 month Euribor) and a margin of 6%.

We'll set the LGD to a constant value of 0.5. Generally LGD often ranges between 20% to 60% depending on the type of loan and collateral. For unsecured loans, LGD tends to be higher (closer to or exceeding 50%) due to the lack of recoverable assets.

Calibrated Probabilities mean:  8.35%
Loss Given Default (LGD):  0.5
Expected Loss as % (EL):  0.0417
Real Loss as % (EL):  0.0404
Average Interest Rates: 10.174%
Average Interest Rates: 10.037%

Asigning Loan Grades

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))

The chart shows the performance of the if only individual with stroke Prob. > T are selected. Additionally the overlay indicates the number of people whose predicted P is in an given range. The overlays can be used to selected the most at risk individual based on the probability predicted for them

C:\Users\Paulius\AppData\Local\Temp\ipykernel_38548\1379105272.py:29: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
  .apply(local_utils.calculate_metrics)

'Returns and Default Rate Based on Default Probability Model:'

	default_rate	mean_interest_rate	expected_return
predicted_grades
A	0.004	0.061	0.061
B	0.022	0.072	0.070
C	0.063	0.092	0.086
D	0.167	0.145	0.121
E-F-G	0.418	0.282	0.164

0.010893600416233091

Loan Acception Criteria and Profit Calculation

The final step we can attempt to is to try to calculate the hypothetical real return of Home Credit's loan portfolio if they used our model to employ a more conservative lending strategy (i.e. rejected all loans where the pre-calibrated default probability is > 0.5).

Again this is only an example because we are still using the interest rates calculated using the calibrated default probabilities should it should be replaced with actual interest rates from Home Credit's loan's to make the data actually meaningful.

	Actual	Hypothetical
Total Loan Amount	18503.7355M	13522.2582M
Total Interest Paid	17.3487M	13.7148M
Total Return %	0.09%	0.10%
Default Rate	8.30%	2.96%
Total Loss	1435.5231M	208.7593M
Losses Avoided	None	1226.7638M
Interest Lost	None	-3.6339M
Total Applications Accepted	30752	21442

That being said Total Loss and Losses Avoided figures are likely to be more accurate (we're using an static LGD of 60% which again should be replaced with the real value from Home Credit's data) than the total return. We can see that we could effectively reduce the total losses by ~80% by ~30% less applications.

Explainability

We've used LGBM which is relatively complex "blackbox" model which might not be the ideal in loan evaluations and similar tasks because it's hard to objectively explain the specific decisions the model made (based on regulatory or customer related requirements).

However, we believe that we were largely able to overcome this shortcoming through the use of single observation SHAP plots:

they allow us to attribute the impact of specific feature (e.g. credit scores, client income etc.) on the estimated risk which allows to select an appropriate grade, interest rate and decide whether the loan should or should not be approved based on our acceptable risk preferences:

Each plotted line explains a single model prediction.

The chart above visualizes the model predictions process for individual observations (for a subsample of 100 loans). For a single sample, the line charts the path from the base value to the final predicted value. ach feature causes the line to shift up or down. This shift is determined by its SHAP value. indicates a feature pushing the prediction higher, while blue indicates it pushes the prediction lower.

Individual Predictions

Below we have included some individuals predictions (focusing a the least accurate, most accurate and some random cases in between). In theory these plots can be used as "continious" decision tree and used to explain why a specific loan was accepted or not for regulatory or other purposes.

e.g. Actual Y Value: 1, Predicted Propability: 0.96 indicates that the application had payment difficulties and that our model predicted this outcome with a 96% likelihood.

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\shap\explainers\_tree.py:448: UserWarning: LightGBM binary classifier with TreeExplainer shap values output has changed to a list of ndarray
  warnings.warn('LightGBM binary classifier with TreeExplainer shap values output has changed to a list of ndarray')

Appendix

Full DS size: 307511

'Distribution of Samples'

dict_keys(['LGBM_Dart_AUC_NEW'])

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\entityset\entityset.py:1914: UserWarning: index SK_BUREAU_ID not found in dataframe, creating new integer column
  warnings.warn(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\computational_backends\feature_set_calculator.py:828: FutureWarning: The provided callable <function min at 0x000001D77E574400> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
  ).agg(to_agg)
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\computational_backends\feature_set_calculator.py:828: FutureWarning: The provided callable <function max at 0x000001D77E5742C0> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
  ).agg(to_agg)
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\computational_backends\feature_set_calculator.py:828: FutureWarning: The provided callable <function mean at 0x000001D77E574CC0> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
  ).agg(to_agg)

Appending previous history

[I 2024-04-30 14:10:40,243] A new study created in memory with name: no-name-4805c4d6-1ec9-4777-906c-84869c0c80c2

drop drop_cols_post_proc: 227
after drop_cols_post_proc: 121
Full DS size: 307511
Tunning: - transformers: transformers: 0
 total options: 0
[]
search_grid:
{}

 - model: LGBM_Dart_AUC_NEW n_iters=10 with:
 {'model__class_weight': ['balanced', None], 'model__objective': ['binary'], 'model__boosting_type': ['gbdt', 'rf', 'dart'], 'model__n_estimators': Range(50, 1000, 50, int), 'model__learning_rate': Range(0.01, 0.3, 0.01, float), 'model__max_depth': Range(3, 11, 1, int), 'model__num_leaves': Range(8, 256, 8, int), 'model__min_gain_to_split': Range(0.0, 15.0, 0.5, float), 'model__min_data_in_leaf': Range(0, 3000, 100, int), 'model__lambda_l1': Range(0, 110, 5, int), 'model__lambda_l2': Range(0, 110, 5, int), 'model__bagging_fraction': Range(0.2, 1.0, 0.1, float), 'model__feature_fraction': Range(0.2, 1.0, 0.1, float), 'model__max_bin': Range(50, 500, 25, int), 'model__drop_rate': Range(0.0, 1.0, 0.025, float)}
Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7248, std_test_score:0.00209 train_set_score:0.7308
folds val/train: [0.727, 0.7253, 0.7214, 0.7266, 0.7235] / [0.7308, 0.7311, 0.732, 0.7298, 0.7304], mean fold time: 1.24

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
[I 2024-04-30 14:10:47,163] Trial 0 finished with value: 0.7247591221654156 and parameters: {'model__class_weight': None, 'model__objective': 'binary', 'model__boosting_type': 'rf', 'model__n_estimators': 900, 'model__learning_rate': 0.08, 'model__max_depth': 11, 'model__num_leaves': 112, 'model__min_gain_to_split': 12.5, 'model__min_data_in_leaf': 2700, 'model__lambda_l1': 50, 'model__lambda_l2': 15, 'model__bagging_fraction': 0.4, 'model__feature_fraction': 0.8, 'model__max_bin': 225, 'model__drop_rate': 0.55}. Best is trial 0 with value: 0.7247591221654156.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7332, std_test_score:0.00291 train_set_score:0.7374
folds val/train: [0.7347, 0.732, 0.729, 0.7378, 0.7323] / [0.7368, 0.7383, 0.7361, 0.7382, 0.7374], mean fold time: 5.41

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
[I 2024-04-30 14:11:14,958] Trial 1 finished with value: 0.733164335671028 and parameters: {'model__class_weight': None, 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 500, 'model__learning_rate': 0.01, 'model__max_depth': 4, 'model__num_leaves': 216, 'model__min_gain_to_split': 7.0, 'model__min_data_in_leaf': 1400, 'model__lambda_l1': 60, 'model__lambda_l2': 75, 'model__bagging_fraction': 0.5, 'model__feature_fraction': 0.30000000000000004, 'model__max_bin': 200, 'model__drop_rate': 0.05}. Best is trial 1 with value: 0.733164335671028.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7266, std_test_score:0.00277 train_set_score:0.7356
folds val/train: [0.7285, 0.7245, 0.7238, 0.7311, 0.7252] / [0.7353, 0.7356, 0.7359, 0.7363, 0.7348], mean fold time: 1.25

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
[I 2024-04-30 14:11:21,928] Trial 2 finished with value: 0.726644314510903 and parameters: {'model__class_weight': None, 'model__objective': 'binary', 'model__boosting_type': 'rf', 'model__n_estimators': 750, 'model__learning_rate': 0.2, 'model__max_depth': 7, 'model__num_leaves': 160, 'model__min_gain_to_split': 10.5, 'model__min_data_in_leaf': 1500, 'model__lambda_l1': 30, 'model__lambda_l2': 20, 'model__bagging_fraction': 0.9000000000000001, 'model__feature_fraction': 0.2, 'model__max_bin': 175, 'model__drop_rate': 0.30000000000000004}. Best is trial 1 with value: 0.733164335671028.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7681, std_test_score:0.00153 train_set_score:0.8038
folds val/train: [0.7694, 0.7654, 0.7673, 0.7693, 0.769] / [0.8039, 0.8033, 0.8042, 0.8038, 0.8038], mean fold time: 21.81

[I 2024-04-30 14:13:11,758] Trial 3 finished with value: 0.7680802042485764 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 350, 'model__learning_rate': 0.08, 'model__max_depth': 9, 'model__num_leaves': 144, 'model__min_gain_to_split': 7.5, 'model__min_data_in_leaf': 700, 'model__lambda_l1': 90, 'model__lambda_l2': 0, 'model__bagging_fraction': 0.6000000000000001, 'model__feature_fraction': 0.4, 'model__max_bin': 375, 'model__drop_rate': 0.6000000000000001}. Best is trial 3 with value: 0.7680802042485764.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7713, std_test_score:0.00130 train_set_score:0.8122
folds val/train: [0.7734, 0.7696, 0.7704, 0.771, 0.7718] / [0.8116, 0.8118, 0.8124, 0.813, 0.8122], mean fold time: 2.36

[I 2024-04-30 14:13:24,340] Trial 4 finished with value: 0.771269722930656 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'gbdt', 'model__n_estimators': 950, 'model__learning_rate': 0.11, 'model__max_depth': 6, 'model__num_leaves': 224, 'model__min_gain_to_split': 13.0, 'model__min_data_in_leaf': 0, 'model__lambda_l1': 15, 'model__lambda_l2': 80, 'model__bagging_fraction': 0.9000000000000001, 'model__feature_fraction': 0.4, 'model__max_bin': 500, 'model__drop_rate': 0.875}. Best is trial 4 with value: 0.771269722930656.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7296, std_test_score:0.00302 train_set_score:0.7395
folds val/train: [0.7331, 0.7296, 0.7247, 0.7322, 0.7283] / [0.7379, 0.7399, 0.7407, 0.7397, 0.7391], mean fold time: 1.35

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
[I 2024-04-30 14:13:31,840] Trial 5 finished with value: 0.7295734635518236 and parameters: {'model__class_weight': None, 'model__objective': 'binary', 'model__boosting_type': 'rf', 'model__n_estimators': 600, 'model__learning_rate': 0.17, 'model__max_depth': 11, 'model__num_leaves': 256, 'model__min_gain_to_split': 4.0, 'model__min_data_in_leaf': 300, 'model__lambda_l1': 110, 'model__lambda_l2': 85, 'model__bagging_fraction': 1.0, 'model__feature_fraction': 0.7, 'model__max_bin': 275, 'model__drop_rate': 0.325}. Best is trial 4 with value: 0.771269722930656.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7702, std_test_score:0.00128 train_set_score:0.8221
folds val/train: [0.7725, 0.7697, 0.7689, 0.7692, 0.7706] / [0.8388, 0.8306, 0.8252, 0.8057, 0.81], mean fold time: 2.27

[I 2024-04-30 14:13:44,003] Trial 6 finished with value: 0.7701777822813336 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'gbdt', 'model__n_estimators': 450, 'model__learning_rate': 0.24000000000000002, 'model__max_depth': 8, 'model__num_leaves': 24, 'model__min_gain_to_split': 4.5, 'model__min_data_in_leaf': 2800, 'model__lambda_l1': 0, 'model__lambda_l2': 70, 'model__bagging_fraction': 0.30000000000000004, 'model__feature_fraction': 1.0, 'model__max_bin': 450, 'model__drop_rate': 0.225}. Best is trial 4 with value: 0.771269722930656.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7646, std_test_score:0.00148 train_set_score:0.7801
folds val/train: [0.765, 0.7623, 0.7635, 0.7657, 0.7664] / [0.7801, 0.7805, 0.7804, 0.78, 0.7794], mean fold time: 3.33

[I 2024-04-30 14:14:01,738] Trial 7 finished with value: 0.7645755916988988 and parameters: {'model__class_weight': None, 'model__objective': 'binary', 'model__boosting_type': 'gbdt', 'model__n_estimators': 850, 'model__learning_rate': 0.11, 'model__max_depth': 8, 'model__num_leaves': 192, 'model__min_gain_to_split': 14.0, 'model__min_data_in_leaf': 2600, 'model__lambda_l1': 25, 'model__lambda_l2': 105, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.9000000000000001, 'model__max_bin': 225, 'model__drop_rate': 0.17500000000000002}. Best is trial 4 with value: 0.771269722930656.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7633, std_test_score:0.00208 train_set_score:0.7764
folds val/train: [0.7656, 0.7601, 0.7617, 0.764, 0.765] / [0.7759, 0.7759, 0.7766, 0.777, 0.7768], mean fold time: 1.92

[I 2024-04-30 14:14:12,368] Trial 8 finished with value: 0.7632609111571824 and parameters: {'model__class_weight': None, 'model__objective': 'binary', 'model__boosting_type': 'gbdt', 'model__n_estimators': 550, 'model__learning_rate': 0.28, 'model__max_depth': 5, 'model__num_leaves': 240, 'model__min_gain_to_split': 14.5, 'model__min_data_in_leaf': 0, 'model__lambda_l1': 20, 'model__lambda_l2': 90, 'model__bagging_fraction': 1.0, 'model__feature_fraction': 0.6000000000000001, 'model__max_bin': 100, 'model__drop_rate': 0.9}. Best is trial 4 with value: 0.771269722930656.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7257, std_test_score:0.00200 train_set_score:0.7356
folds val/train: [0.7258, 0.7237, 0.7239, 0.7293, 0.7258] / [0.7357, 0.7354, 0.7359, 0.7356, 0.7351], mean fold time: 2.34

[I 2024-04-30 14:14:25,096] Trial 9 finished with value: 0.7256979936912922 and parameters: {'model__class_weight': None, 'model__objective': 'binary', 'model__boosting_type': 'rf', 'model__n_estimators': 650, 'model__learning_rate': 0.05, 'model__max_depth': 10, 'model__num_leaves': 56, 'model__min_gain_to_split': 7.0, 'model__min_data_in_leaf': 2100, 'model__lambda_l1': 15, 'model__lambda_l2': 5, 'model__bagging_fraction': 0.30000000000000004, 'model__feature_fraction': 0.9000000000000001, 'model__max_bin': 75, 'model__drop_rate': 0.625}. Best is trial 4 with value: 0.771269722930656.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7638, std_test_score:0.00140 train_set_score:0.7937
folds val/train: [0.7656, 0.7613, 0.7638, 0.7642, 0.7643] / [0.7935, 0.7939, 0.794, 0.7938, 0.7933], mean fold time: 1.91

[I 2024-04-30 14:14:35,466] Trial 10 finished with value: 0.7638478396414092 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'gbdt', 'model__n_estimators': 50, 'model__learning_rate': 0.12, 'model__max_depth': 6, 'model__num_leaves': 88, 'model__min_gain_to_split': 1.0, 'model__min_data_in_leaf': 700, 'model__lambda_l1': 50, 'model__lambda_l2': 45, 'model__bagging_fraction': 0.7, 'model__feature_fraction': 0.5, 'model__max_bin': 500, 'model__drop_rate': 0.9750000000000001}. Best is trial 4 with value: 0.771269722930656.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7709, std_test_score:0.00211 train_set_score:0.7988
folds val/train: [0.7743, 0.7703, 0.7685, 0.7691, 0.7722] / [0.807, 0.8008, 0.7974, 0.7913, 0.7973], mean fold time: 2.18

[I 2024-04-30 14:14:47,169] Trial 11 finished with value: 0.7708781891661243 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'gbdt', 'model__n_estimators': 300, 'model__learning_rate': 0.23, 'model__max_depth': 3, 'model__num_leaves': 8, 'model__min_gain_to_split': 3.5, 'model__min_data_in_leaf': 2100, 'model__lambda_l1': 0, 'model__lambda_l2': 60, 'model__bagging_fraction': 0.8, 'model__feature_fraction': 1.0, 'model__max_bin': 475, 'model__drop_rate': 0.775}. Best is trial 4 with value: 0.771269722930656.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7730, std_test_score:0.00203 train_set_score:0.7982
folds val/train: [0.7767, 0.7722, 0.7707, 0.7722, 0.7731] / [0.8062, 0.797, 0.795, 0.7971, 0.7957], mean fold time: 6.12

[I 2024-04-30 14:15:19,006] Trial 12 finished with value: 0.7729736600713923 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'gbdt', 'model__n_estimators': 250, 'model__learning_rate': 0.21000000000000002, 'model__max_depth': 3, 'model__num_leaves': 16, 'model__min_gain_to_split': 0.0, 'model__min_data_in_leaf': 2000, 'model__lambda_l1': 0, 'model__lambda_l2': 50, 'model__bagging_fraction': 0.8, 'model__feature_fraction': 0.5, 'model__max_bin': 375, 'model__drop_rate': 0.8}. Best is trial 12 with value: 0.7729736600713923.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7673, std_test_score:0.00125 train_set_score:0.7823
folds val/train: [0.7684, 0.7661, 0.7661, 0.7666, 0.7692] / [0.782, 0.7827, 0.7823, 0.7819, 0.7825], mean fold time: 3.38

[I 2024-04-30 14:15:36,908] Trial 13 finished with value: 0.7672730632046433 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'gbdt', 'model__n_estimators': 100, 'model__learning_rate': 0.16, 'model__max_depth': 3, 'model__num_leaves': 184, 'model__min_gain_to_split': 0.0, 'model__min_data_in_leaf': 2000, 'model__lambda_l1': 0, 'model__lambda_l2': 40, 'model__bagging_fraction': 0.8, 'model__feature_fraction': 0.5, 'model__max_bin': 400, 'model__drop_rate': 0.775}. Best is trial 12 with value: 0.7729736600713923.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7689, std_test_score:0.00186 train_set_score:0.7956
folds val/train: [0.7712, 0.7661, 0.7674, 0.7699, 0.7697] / [0.7959, 0.7955, 0.7953, 0.7962, 0.7952], mean fold time: 2.21

[I 2024-04-30 14:15:48,764] Trial 14 finished with value: 0.7688614622014904 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'gbdt', 'model__n_estimators': 1000, 'model__learning_rate': 0.3, 'model__max_depth': 5, 'model__num_leaves': 80, 'model__min_gain_to_split': 10.0, 'model__min_data_in_leaf': 1100, 'model__lambda_l1': 65, 'model__lambda_l2': 35, 'model__bagging_fraction': 0.8, 'model__feature_fraction': 0.4, 'model__max_bin': 350, 'model__drop_rate': 0.8}. Best is trial 12 with value: 0.7729736600713923.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7710, std_test_score:0.00142 train_set_score:0.7985
folds val/train: [0.7735, 0.7698, 0.7705, 0.7697, 0.7717] / [0.7988, 0.7982, 0.7994, 0.7984, 0.7977], mean fold time: 1.99

[I 2024-04-30 14:15:59,500] Trial 15 finished with value: 0.7710315875391158 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'gbdt', 'model__n_estimators': 200, 'model__learning_rate': 0.19, 'model__max_depth': 6, 'model__num_leaves': 120, 'model__min_gain_to_split': 10.0, 'model__min_data_in_leaf': 1900, 'model__lambda_l1': 35, 'model__lambda_l2': 60, 'model__bagging_fraction': 0.6000000000000001, 'model__feature_fraction': 0.2, 'model__max_bin': 325, 'model__drop_rate': 1.0}. Best is trial 12 with value: 0.7729736600713923.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7676, std_test_score:0.00100 train_set_score:0.7938
folds val/train: [0.7684, 0.7662, 0.7665, 0.7681, 0.7687] / [0.7934, 0.7942, 0.7944, 0.7936, 0.7934], mean fold time: 38.04

[I 2024-04-30 14:19:10,473] Trial 16 finished with value: 0.7675725766548119 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 300, 'model__learning_rate': 0.13, 'model__max_depth': 4, 'model__num_leaves': 48, 'model__min_gain_to_split': 2.0, 'model__min_data_in_leaf': 1000, 'model__lambda_l1': 10, 'model__lambda_l2': 100, 'model__bagging_fraction': 0.9000000000000001, 'model__feature_fraction': 0.6000000000000001, 'model__max_bin': 425, 'model__drop_rate': 0.42500000000000004}. Best is trial 12 with value: 0.7729736600713923.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7691, std_test_score:0.00187 train_set_score:0.7993
folds val/train: [0.7723, 0.7666, 0.7685, 0.7687, 0.7696] / [0.7991, 0.7992, 0.8001, 0.7993, 0.7987], mean fold time: 1.62

[I 2024-04-30 14:19:19,385] Trial 17 finished with value: 0.7691357518070465 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'gbdt', 'model__n_estimators': 750, 'model__learning_rate': 0.25, 'model__max_depth': 6, 'model__num_leaves': 176, 'model__min_gain_to_split': 11.5, 'model__min_data_in_leaf': 2400, 'model__lambda_l1': 40, 'model__lambda_l2': 75, 'model__bagging_fraction': 0.7, 'model__feature_fraction': 0.4, 'model__max_bin': 300, 'model__drop_rate': 0.7000000000000001}. Best is trial 12 with value: 0.7729736600713923.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7712, std_test_score:0.00147 train_set_score:0.7943
folds val/train: [0.7729, 0.7687, 0.7706, 0.7714, 0.7724] / [0.7932, 0.7941, 0.796, 0.7939, 0.7941], mean fold time: 2.13

[I 2024-04-30 14:19:31,234] Trial 18 finished with value: 0.7712175359581087 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'gbdt', 'model__n_estimators': 200, 'model__learning_rate': 0.19, 'model__max_depth': 4, 'model__num_leaves': 216, 'model__min_gain_to_split': 8.0, 'model__min_data_in_leaf': 1500, 'model__lambda_l1': 75, 'model__lambda_l2': 45, 'model__bagging_fraction': 0.9000000000000001, 'model__feature_fraction': 0.5, 'model__max_bin': 500, 'model__drop_rate': 0.875}. Best is trial 12 with value: 0.7729736600713923.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7732, std_test_score:0.00156 train_set_score:0.8209
folds val/train: [0.7756, 0.7711, 0.7719, 0.7735, 0.7739] / [0.8221, 0.8203, 0.8207, 0.8213, 0.8203], mean fold time: 25.83

[I 2024-04-30 14:21:41,235] Trial 19 finished with value: 0.7732021037088023 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 400, 'model__learning_rate': 0.14, 'model__max_depth': 7, 'model__num_leaves': 88, 'model__min_gain_to_split': 13.5, 'model__min_data_in_leaf': 0, 'model__lambda_l1': 10, 'model__lambda_l2': 90, 'model__bagging_fraction': 0.7, 'model__feature_fraction': 0.30000000000000004, 'model__max_bin': 425, 'model__drop_rate': 0.45}. Best is trial 19 with value: 0.7732021037088023.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7737, std_test_score:0.00150 train_set_score:0.8402
folds val/train: [0.7753, 0.7722, 0.7717, 0.7744, 0.7751] / [0.8404, 0.8396, 0.8411, 0.8404, 0.8397], mean fold time: 19.83

[I 2024-04-30 14:23:21,211] Trial 20 finished with value: 0.773736619748256 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 400, 'model__learning_rate': 0.22, 'model__max_depth': 8, 'model__num_leaves': 40, 'model__min_gain_to_split': 5.0, 'model__min_data_in_leaf': 3000, 'model__lambda_l1': 10, 'model__lambda_l2': 110, 'model__bagging_fraction': 0.5, 'model__feature_fraction': 0.30000000000000004, 'model__max_bin': 425, 'model__drop_rate': 0.45}. Best is trial 20 with value: 0.773736619748256.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7745, std_test_score:0.00123 train_set_score:0.8443
folds val/train: [0.776, 0.7732, 0.7729, 0.7746, 0.7756] / [0.8443, 0.843, 0.844, 0.8456, 0.8444], mean fold time: 19.61

[I 2024-04-30 14:25:00,094] Trial 21 finished with value: 0.7744710826089047 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 450, 'model__learning_rate': 0.23, 'model__max_depth': 8, 'model__num_leaves': 48, 'model__min_gain_to_split': 5.5, 'model__min_data_in_leaf': 2900, 'model__lambda_l1': 10, 'model__lambda_l2': 110, 'model__bagging_fraction': 0.5, 'model__feature_fraction': 0.30000000000000004, 'model__max_bin': 400, 'model__drop_rate': 0.42500000000000004}. Best is trial 21 with value: 0.7744710826089047.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7732, std_test_score:0.00163 train_set_score:0.8425
folds val/train: [0.7754, 0.7708, 0.7719, 0.7734, 0.7744] / [0.8433, 0.8433, 0.8425, 0.8428, 0.8406], mean fold time: 18.03

[I 2024-04-30 14:26:31,031] Trial 22 finished with value: 0.7731975885382104 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 400, 'model__learning_rate': 0.25, 'model__max_depth': 8, 'model__num_leaves': 56, 'model__min_gain_to_split': 5.5, 'model__min_data_in_leaf': 2900, 'model__lambda_l1': 10, 'model__lambda_l2': 110, 'model__bagging_fraction': 0.5, 'model__feature_fraction': 0.30000000000000004, 'model__max_bin': 450, 'model__drop_rate': 0.42500000000000004}. Best is trial 21 with value: 0.7744710826089047.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7736, std_test_score:0.00140 train_set_score:0.8264
folds val/train: [0.7753, 0.7721, 0.7719, 0.7739, 0.7748] / [0.8266, 0.8246, 0.8273, 0.8272, 0.8262], mean fold time: 20.32

[I 2024-04-30 14:28:13,425] Trial 23 finished with value: 0.7736188555317012 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 400, 'model__learning_rate': 0.27, 'model__max_depth': 9, 'model__num_leaves': 80, 'model__min_gain_to_split': 5.5, 'model__min_data_in_leaf': 2400, 'model__lambda_l1': 40, 'model__lambda_l2': 95, 'model__bagging_fraction': 0.5, 'model__feature_fraction': 0.30000000000000004, 'model__max_bin': 425, 'model__drop_rate': 0.45}. Best is trial 21 with value: 0.7744710826089047.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7743, std_test_score:0.00114 train_set_score:0.8227
folds val/train: [0.7754, 0.7741, 0.7725, 0.7737, 0.7756] / [0.8235, 0.8217, 0.8219, 0.8236, 0.8225], mean fold time: 24.82

[I 2024-04-30 14:30:18,349] Trial 24 finished with value: 0.7742569892640564 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 500, 'model__learning_rate': 0.28, 'model__max_depth': 9, 'model__num_leaves': 40, 'model__min_gain_to_split': 5.5, 'model__min_data_in_leaf': 3000, 'model__lambda_l1': 40, 'model__lambda_l2': 100, 'model__bagging_fraction': 0.5, 'model__feature_fraction': 0.30000000000000004, 'model__max_bin': 350, 'model__drop_rate': 0.35000000000000003}. Best is trial 21 with value: 0.7744710826089047.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7730, std_test_score:0.00176 train_set_score:0.8582
folds val/train: [0.776, 0.7708, 0.772, 0.7735, 0.7727] / [0.8573, 0.8581, 0.859, 0.8597, 0.8572], mean fold time: 35.85

[I 2024-04-30 14:33:18,399] Trial 25 finished with value: 0.772998044701975 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 700, 'model__learning_rate': 0.3, 'model__max_depth': 9, 'model__num_leaves': 40, 'model__min_gain_to_split': 2.5, 'model__min_data_in_leaf': 3000, 'model__lambda_l1': 25, 'model__lambda_l2': 110, 'model__bagging_fraction': 0.4, 'model__feature_fraction': 0.2, 'model__max_bin': 375, 'model__drop_rate': 0.325}. Best is trial 21 with value: 0.7744710826089047.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7740, std_test_score:0.00162 train_set_score:0.8036
folds val/train: [0.7761, 0.7716, 0.7726, 0.7746, 0.7748] / [0.8038, 0.8028, 0.804, 0.8039, 0.8036], mean fold time: 19.51

[I 2024-04-30 14:34:56,749] Trial 26 finished with value: 0.7739512652732408 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 500, 'model__learning_rate': 0.22, 'model__max_depth': 10, 'model__num_leaves': 32, 'model__min_gain_to_split': 8.5, 'model__min_data_in_leaf': 2500, 'model__lambda_l1': 45, 'model__lambda_l2': 100, 'model__bagging_fraction': 0.4, 'model__feature_fraction': 0.2, 'model__max_bin': 325, 'model__drop_rate': 0.15000000000000002}. Best is trial 21 with value: 0.7744710826089047.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7738, std_test_score:0.00148 train_set_score:0.7986
folds val/train: [0.7757, 0.772, 0.7726, 0.7734, 0.7754] / [0.7985, 0.7979, 0.7994, 0.7984, 0.7986], mean fold time: 5.78

[I 2024-04-30 14:35:26,433] Trial 27 finished with value: 0.7738168190120491 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 550, 'model__learning_rate': 0.27, 'model__max_depth': 10, 'model__num_leaves': 64, 'model__min_gain_to_split': 8.0, 'model__min_data_in_leaf': 2500, 'model__lambda_l1': 75, 'model__lambda_l2': 100, 'model__bagging_fraction': 0.4, 'model__feature_fraction': 0.2, 'model__max_bin': 300, 'model__drop_rate': 0.025}. Best is trial 21 with value: 0.7744710826089047.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7751, std_test_score:0.00160 train_set_score:0.8100
folds val/train: [0.7774, 0.7736, 0.7732, 0.7748, 0.7763] / [0.8103, 0.8095, 0.81, 0.81, 0.8101], mean fold time: 21.19

[I 2024-04-30 14:37:13,206] Trial 28 finished with value: 0.77509084497967 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 600, 'model__learning_rate': 0.26, 'model__max_depth': 10, 'model__num_leaves': 24, 'model__min_gain_to_split': 6.0, 'model__min_data_in_leaf': 2300, 'model__lambda_l1': 45, 'model__lambda_l2': 95, 'model__bagging_fraction': 0.30000000000000004, 'model__feature_fraction': 0.2, 'model__max_bin': 325, 'model__drop_rate': 0.125}. Best is trial 28 with value: 0.77509084497967.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7750, std_test_score:0.00135 train_set_score:0.8244
folds val/train: [0.7767, 0.7738, 0.7732, 0.7752, 0.7762] / [0.824, 0.8238, 0.8252, 0.8246, 0.8244], mean fold time: 33.81

[I 2024-04-30 14:40:03,043] Trial 29 finished with value: 0.7750172313526884 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 850, 'model__learning_rate': 0.28, 'model__max_depth': 11, 'model__num_leaves': 112, 'model__min_gain_to_split': 6.0, 'model__min_data_in_leaf': 2700, 'model__lambda_l1': 55, 'model__lambda_l2': 65, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.7, 'model__max_bin': 250, 'model__drop_rate': 0.125}. Best is trial 28 with value: 0.77509084497967.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7753, std_test_score:0.00151 train_set_score:0.8275
folds val/train: [0.7768, 0.7735, 0.7734, 0.7762, 0.7765] / [0.8279, 0.8264, 0.8277, 0.8272, 0.8282], mean fold time: 31.25

[I 2024-04-30 14:42:40,077] Trial 30 finished with value: 0.7753115326000964 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 850, 'model__learning_rate': 0.26, 'model__max_depth': 11, 'model__num_leaves': 104, 'model__min_gain_to_split': 6.0, 'model__min_data_in_leaf': 2300, 'model__lambda_l1': 55, 'model__lambda_l2': 70, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.7, 'model__max_bin': 250, 'model__drop_rate': 0.1}. Best is trial 30 with value: 0.7753115326000964.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7751, std_test_score:0.00126 train_set_score:0.8239
folds val/train: [0.7762, 0.7733, 0.7739, 0.7759, 0.7763] / [0.8239, 0.8226, 0.8247, 0.8238, 0.8243], mean fold time: 25.30

[I 2024-04-30 14:44:47,397] Trial 31 finished with value: 0.7751128635029536 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 800, 'model__learning_rate': 0.26, 'model__max_depth': 11, 'model__num_leaves': 112, 'model__min_gain_to_split': 6.5, 'model__min_data_in_leaf': 2700, 'model__lambda_l1': 55, 'model__lambda_l2': 65, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.7, 'model__max_bin': 250, 'model__drop_rate': 0.1}. Best is trial 30 with value: 0.7753115326000964.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7746, std_test_score:0.00142 train_set_score:0.8232
folds val/train: [0.7762, 0.7731, 0.7728, 0.7752, 0.7759] / [0.8235, 0.8224, 0.8235, 0.824, 0.8226], mean fold time: 31.65

[I 2024-04-30 14:47:26,417] Trial 32 finished with value: 0.7746302453033435 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 850, 'model__learning_rate': 0.26, 'model__max_depth': 11, 'model__num_leaves': 112, 'model__min_gain_to_split': 6.5, 'model__min_data_in_leaf': 2200, 'model__lambda_l1': 60, 'model__lambda_l2': 65, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.7, 'model__max_bin': 225, 'model__drop_rate': 0.1}. Best is trial 30 with value: 0.7753115326000964.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7673, std_test_score:0.00113 train_set_score:0.8098
folds val/train: [0.7686, 0.7672, 0.7655, 0.7684, 0.7668] / [0.8096, 0.8101, 0.8097, 0.8109, 0.8087], mean fold time: 3.90

[I 2024-04-30 14:47:46,728] Trial 33 finished with value: 0.7673077353749032 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 850, 'model__learning_rate': 0.29000000000000004, 'model__max_depth': 11, 'model__num_leaves': 136, 'model__min_gain_to_split': 9.0, 'model__min_data_in_leaf': 1700, 'model__lambda_l1': 70, 'model__lambda_l2': 30, 'model__bagging_fraction': 0.30000000000000004, 'model__feature_fraction': 0.7, 'model__max_bin': 175, 'model__drop_rate': 0.0}. Best is trial 30 with value: 0.7753115326000964.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7692, std_test_score:0.00168 train_set_score:0.7857
folds val/train: [0.77, 0.7671, 0.7685, 0.7683, 0.772] / [0.7857, 0.7859, 0.7858, 0.7855, 0.7855], mean fold time: 14.13

[I 2024-04-30 14:48:58,149] Trial 34 finished with value: 0.7691855336779547 and parameters: {'model__class_weight': None, 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 750, 'model__learning_rate': 0.26, 'model__max_depth': 11, 'model__num_leaves': 104, 'model__min_gain_to_split': 7.0, 'model__min_data_in_leaf': 2700, 'model__lambda_l1': 55, 'model__lambda_l2': 70, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.8, 'model__max_bin': 250, 'model__drop_rate': 0.1}. Best is trial 30 with value: 0.7753115326000964.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7284, std_test_score:0.00217 train_set_score:0.7386
folds val/train: [0.7307, 0.7277, 0.7256, 0.7311, 0.7267] / [0.7396, 0.7361, 0.7395, 0.74, 0.7379], mean fold time: 1.27

[I 2024-04-30 14:49:05,334] Trial 35 finished with value: 0.7283693254791357 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'rf', 'model__n_estimators': 900, 'model__learning_rate': 0.24000000000000002, 'model__max_depth': 10, 'model__num_leaves': 152, 'model__min_gain_to_split': 6.5, 'model__min_data_in_leaf': 2300, 'model__lambda_l1': 85, 'model__lambda_l2': 55, 'model__bagging_fraction': 0.30000000000000004, 'model__feature_fraction': 0.8, 'model__max_bin': 150, 'model__drop_rate': 0.25}. Best is trial 30 with value: 0.7753115326000964.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7613, std_test_score:0.00115 train_set_score:0.7926
folds val/train: [0.763, 0.7597, 0.7604, 0.7616, 0.7619] / [0.7923, 0.7929, 0.7932, 0.7923, 0.7924], mean fold time: 35.00

[I 2024-04-30 14:52:01,167] Trial 36 finished with value: 0.761320075192292 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 800, 'model__learning_rate': 0.02, 'model__max_depth': 11, 'model__num_leaves': 120, 'model__min_gain_to_split': 3.0, 'model__min_data_in_leaf': 2700, 'model__lambda_l1': 55, 'model__lambda_l2': 80, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.7, 'model__max_bin': 250, 'model__drop_rate': 0.1}. Best is trial 30 with value: 0.7753115326000964.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7706, std_test_score:0.00207 train_set_score:0.7892
folds val/train: [0.7724, 0.7671, 0.7699, 0.7707, 0.7729] / [0.789, 0.7894, 0.79, 0.7891, 0.7883], mean fold time: 28.08

[I 2024-04-30 14:54:22,373] Trial 37 finished with value: 0.7705940846273202 and parameters: {'model__class_weight': None, 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 950, 'model__learning_rate': 0.2, 'model__max_depth': 10, 'model__num_leaves': 168, 'model__min_gain_to_split': 4.0, 'model__min_data_in_leaf': 1800, 'model__lambda_l1': 60, 'model__lambda_l2': 75, 'model__bagging_fraction': 0.30000000000000004, 'model__feature_fraction': 0.6000000000000001, 'model__max_bin': 200, 'model__drop_rate': 0.17500000000000002}. Best is trial 30 with value: 0.7753115326000964.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7297, std_test_score:0.00235 train_set_score:0.7412
folds val/train: [0.7317, 0.7321, 0.7262, 0.7309, 0.7276] / [0.7405, 0.7432, 0.7418, 0.7413, 0.7393], mean fold time: 1.43

[I 2024-04-30 14:54:30,338] Trial 38 finished with value: 0.7297004724664784 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'rf', 'model__n_estimators': 650, 'model__learning_rate': 0.28, 'model__max_depth': 11, 'model__num_leaves': 104, 'model__min_gain_to_split': 6.0, 'model__min_data_in_leaf': 2600, 'model__lambda_l1': 50, 'model__lambda_l2': 65, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.8, 'model__max_bin': 275, 'model__drop_rate': 0.05}. Best is trial 30 with value: 0.7753115326000964.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7665, std_test_score:0.00171 train_set_score:0.7786
folds val/train: [0.7674, 0.7633, 0.7664, 0.7668, 0.7684] / [0.7778, 0.7792, 0.779, 0.779, 0.778], mean fold time: 29.17

[I 2024-04-30 14:56:56,973] Trial 39 finished with value: 0.7664675879453234 and parameters: {'model__class_weight': None, 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 1000, 'model__learning_rate': 0.18000000000000002, 'model__max_depth': 10, 'model__num_leaves': 136, 'model__min_gain_to_split': 9.0, 'model__min_data_in_leaf': 2300, 'model__lambda_l1': 85, 'model__lambda_l2': 85, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.9000000000000001, 'model__max_bin': 250, 'model__drop_rate': 0.275}. Best is trial 30 with value: 0.7753115326000964.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7747, std_test_score:0.00157 train_set_score:0.8531
folds val/train: [0.7768, 0.7736, 0.7725, 0.7746, 0.7762] / [0.8536, 0.8507, 0.8539, 0.8545, 0.8528], mean fold time: 41.99

[I 2024-04-30 15:00:27,725] Trial 40 finished with value: 0.7747473413777572 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 800, 'model__learning_rate': 0.09, 'model__max_depth': 11, 'model__num_leaves': 144, 'model__min_gain_to_split': 4.5, 'model__min_data_in_leaf': 2800, 'model__lambda_l1': 30, 'model__lambda_l2': 80, 'model__bagging_fraction': 0.30000000000000004, 'model__feature_fraction': 0.7, 'model__max_bin': 150, 'model__drop_rate': 0.17500000000000002}. Best is trial 30 with value: 0.7753115326000964.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7740, std_test_score:0.00138 train_set_score:0.8519
folds val/train: [0.7749, 0.7722, 0.7725, 0.7749, 0.7755] / [0.8522, 0.8509, 0.8521, 0.8517, 0.8524], mean fold time: 42.51

[I 2024-04-30 15:04:01,067] Trial 41 finished with value: 0.7740293847140816 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 800, 'model__learning_rate': 0.08, 'model__max_depth': 11, 'model__num_leaves': 160, 'model__min_gain_to_split': 4.0, 'model__min_data_in_leaf': 2800, 'model__lambda_l1': 30, 'model__lambda_l2': 80, 'model__bagging_fraction': 0.30000000000000004, 'model__feature_fraction': 0.7, 'model__max_bin': 125, 'model__drop_rate': 0.2}. Best is trial 30 with value: 0.7753115326000964.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7746, std_test_score:0.00136 train_set_score:0.8379
folds val/train: [0.7759, 0.7727, 0.7734, 0.775, 0.7761] / [0.8379, 0.837, 0.8387, 0.8386, 0.8375], mean fold time: 46.51

[I 2024-04-30 15:07:54,430] Trial 42 finished with value: 0.7745977539160103 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 900, 'model__learning_rate': 0.09, 'model__max_depth': 10, 'model__num_leaves': 200, 'model__min_gain_to_split': 4.5, 'model__min_data_in_leaf': 2600, 'model__lambda_l1': 45, 'model__lambda_l2': 70, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.6000000000000001, 'model__max_bin': 50, 'model__drop_rate': 0.125}. Best is trial 30 with value: 0.7753115326000964.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7676, std_test_score:0.00122 train_set_score:0.8086
folds val/train: [0.769, 0.7656, 0.7669, 0.7681, 0.7684] / [0.8083, 0.8088, 0.809, 0.8084, 0.8085], mean fold time: 19.67

[I 2024-04-30 15:09:33,588] Trial 43 finished with value: 0.7676253286972854 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 700, 'model__learning_rate': 0.04, 'model__max_depth': 11, 'model__num_leaves': 144, 'model__min_gain_to_split': 7.5, 'model__min_data_in_leaf': 2800, 'model__lambda_l1': 50, 'model__lambda_l2': 85, 'model__bagging_fraction': 0.30000000000000004, 'model__feature_fraction': 0.7, 'model__max_bin': 200, 'model__drop_rate': 0.05}. Best is trial 30 with value: 0.7753115326000964.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7745, std_test_score:0.00191 train_set_score:0.8295
folds val/train: [0.7767, 0.7721, 0.7724, 0.7751, 0.7762] / [0.8308, 0.8277, 0.8299, 0.8302, 0.8288], mean fold time: 42.27

[I 2024-04-30 15:13:05,738] Trial 44 finished with value: 0.7744982103103919 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 800, 'model__learning_rate': 0.09999999999999999, 'model__max_depth': 11, 'model__num_leaves': 128, 'model__min_gain_to_split': 6.5, 'model__min_data_in_leaf': 2200, 'model__lambda_l1': 65, 'model__lambda_l2': 55, 'model__bagging_fraction': 0.4, 'model__feature_fraction': 0.8, 'model__max_bin': 175, 'model__drop_rate': 0.2}. Best is trial 30 with value: 0.7753115326000964.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7257, std_test_score:0.00113 train_set_score:0.7372
folds val/train: [0.7272, 0.7255, 0.7238, 0.7264, 0.7257] / [0.7361, 0.7371, 0.7386, 0.7369, 0.7372], mean fold time: 1.43

[I 2024-04-30 15:13:13,707] Trial 45 finished with value: 0.7257222600803734 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'rf', 'model__n_estimators': 650, 'model__learning_rate': 0.060000000000000005, 'model__max_depth': 10, 'model__num_leaves': 104, 'model__min_gain_to_split': 4.5, 'model__min_data_in_leaf': 2500, 'model__lambda_l1': 30, 'model__lambda_l2': 90, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.9000000000000001, 'model__max_bin': 275, 'model__drop_rate': 0.07500000000000001}. Best is trial 30 with value: 0.7753115326000964.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7688, std_test_score:0.00178 train_set_score:0.7853
folds val/train: [0.7693, 0.7664, 0.7687, 0.768, 0.7719] / [0.7844, 0.7861, 0.7861, 0.785, 0.7849], mean fold time: 24.21

[I 2024-04-30 15:15:15,565] Trial 46 finished with value: 0.7688409152353183 and parameters: {'model__class_weight': None, 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 900, 'model__learning_rate': 0.25, 'model__max_depth': 11, 'model__num_leaves': 80, 'model__min_gain_to_split': 7.5, 'model__min_data_in_leaf': 2700, 'model__lambda_l1': 35, 'model__lambda_l2': 65, 'model__bagging_fraction': 0.30000000000000004, 'model__feature_fraction': 0.7, 'model__max_bin': 150, 'model__drop_rate': 0.275}. Best is trial 30 with value: 0.7753115326000964.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7724, std_test_score:0.00087 train_set_score:0.8930
folds val/train: [0.7736, 0.771, 0.7721, 0.7728, 0.7726] / [0.8932, 0.8926, 0.8927, 0.8945, 0.8922], mean fold time: 31.05

[I 2024-04-30 15:17:51,606] Trial 47 finished with value: 0.7724475816250334 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 600, 'model__learning_rate': 0.29000000000000004, 'model__max_depth': 9, 'model__num_leaves': 96, 'model__min_gain_to_split': 1.5, 'model__min_data_in_leaf': 1600, 'model__lambda_l1': 45, 'model__lambda_l2': 60, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.6000000000000001, 'model__max_bin': 300, 'model__drop_rate': 0.15000000000000002}. Best is trial 30 with value: 0.7753115326000964.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7757, std_test_score:0.00143 train_set_score:0.8445
folds val/train: [0.7777, 0.7738, 0.7745, 0.776, 0.7766] / [0.845, 0.8431, 0.8453, 0.8455, 0.8438], mean fold time: 12.68

[I 2024-04-30 15:18:55,783] Trial 48 finished with value: 0.7757164745074661 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 700, 'model__learning_rate': 0.15000000000000002, 'model__max_depth': 10, 'model__num_leaves': 152, 'model__min_gain_to_split': 3.5, 'model__min_data_in_leaf': 2300, 'model__lambda_l1': 65, 'model__lambda_l2': 75, 'model__bagging_fraction': 0.30000000000000004, 'model__feature_fraction': 0.7, 'model__max_bin': 225, 'model__drop_rate': 0.025}. Best is trial 48 with value: 0.7757164745074661.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7285, std_test_score:0.00233 train_set_score:0.7397
folds val/train: [0.7317, 0.7282, 0.7272, 0.7302, 0.725] / [0.7398, 0.7388, 0.7411, 0.7393, 0.7397], mean fold time: 1.31

[I 2024-04-30 15:19:03,150] Trial 49 finished with value: 0.728468804419493 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'rf', 'model__n_estimators': 700, 'model__learning_rate': 0.14, 'model__max_depth': 10, 'model__num_leaves': 120, 'model__min_gain_to_split': 3.0, 'model__min_data_in_leaf': 2000, 'model__lambda_l1': 105, 'model__lambda_l2': 50, 'model__bagging_fraction': 0.4, 'model__feature_fraction': 0.8, 'model__max_bin': 225, 'model__drop_rate': 0.0}. Best is trial 48 with value: 0.7757164745074661.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7738, std_test_score:0.00124 train_set_score:0.8446
folds val/train: [0.7754, 0.7723, 0.7725, 0.7741, 0.7748] / [0.8454, 0.8432, 0.8451, 0.8457, 0.8436], mean fold time: 33.21

[I 2024-04-30 15:21:49,990] Trial 50 finished with value: 0.773824418539814 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 600, 'model__learning_rate': 0.16, 'model__max_depth': 9, 'model__num_leaves': 72, 'model__min_gain_to_split': 3.5, 'model__min_data_in_leaf': 1300, 'model__lambda_l1': 65, 'model__lambda_l2': 75, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.5, 'model__max_bin': 250, 'model__drop_rate': 0.55}. Best is trial 48 with value: 0.7757164745074661.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7755, std_test_score:0.00155 train_set_score:0.8350
folds val/train: [0.7773, 0.7745, 0.773, 0.7762, 0.7764] / [0.8361, 0.8344, 0.8352, 0.8355, 0.8338], mean fold time: 15.63

[I 2024-04-30 15:23:08,949] Trial 51 finished with value: 0.7754731351684023 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 750, 'model__learning_rate': 0.27, 'model__max_depth': 11, 'model__num_leaves': 152, 'model__min_gain_to_split': 5.0, 'model__min_data_in_leaf': 2300, 'model__lambda_l1': 55, 'model__lambda_l2': 80, 'model__bagging_fraction': 0.30000000000000004, 'model__feature_fraction': 0.7, 'model__max_bin': 200, 'model__drop_rate': 0.05}. Best is trial 48 with value: 0.7757164745074661.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7753, std_test_score:0.00161 train_set_score:0.8274
folds val/train: [0.7776, 0.7739, 0.7731, 0.7754, 0.7764] / [0.8272, 0.8268, 0.8275, 0.8285, 0.8268], mean fold time: 15.11

[I 2024-04-30 15:24:25,262] Trial 52 finished with value: 0.7753062983005823 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 750, 'model__learning_rate': 0.27, 'model__max_depth': 11, 'model__num_leaves': 160, 'model__min_gain_to_split': 6.0, 'model__min_data_in_leaf': 2200, 'model__lambda_l1': 55, 'model__lambda_l2': 70, 'model__bagging_fraction': 0.30000000000000004, 'model__feature_fraction': 0.6000000000000001, 'model__max_bin': 200, 'model__drop_rate': 0.05}. Best is trial 48 with value: 0.7757164745074661.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7754, std_test_score:0.00146 train_set_score:0.8263
folds val/train: [0.7777, 0.7737, 0.774, 0.7751, 0.7763] / [0.8273, 0.8249, 0.8264, 0.8274, 0.8255], mean fold time: 16.15

[I 2024-04-30 15:25:46,823] Trial 53 finished with value: 0.7753566828181653 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 750, 'model__learning_rate': 0.24000000000000002, 'model__max_depth': 10, 'model__num_leaves': 160, 'model__min_gain_to_split': 5.0, 'model__min_data_in_leaf': 2200, 'model__lambda_l1': 70, 'model__lambda_l2': 70, 'model__bagging_fraction': 0.30000000000000004, 'model__feature_fraction': 0.6000000000000001, 'model__max_bin': 225, 'model__drop_rate': 0.05}. Best is trial 48 with value: 0.7757164745074661.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7750, std_test_score:0.00138 train_set_score:0.8251
folds val/train: [0.777, 0.7732, 0.7738, 0.7754, 0.7757] / [0.8257, 0.824, 0.8251, 0.8257, 0.8249], mean fold time: 16.04

[I 2024-04-30 15:27:07,820] Trial 54 finished with value: 0.7750219450606673 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 750, 'model__learning_rate': 0.24000000000000002, 'model__max_depth': 11, 'model__num_leaves': 168, 'model__min_gain_to_split': 5.0, 'model__min_data_in_leaf': 2100, 'model__lambda_l1': 75, 'model__lambda_l2': 75, 'model__bagging_fraction': 0.4, 'model__feature_fraction': 0.6000000000000001, 'model__max_bin': 225, 'model__drop_rate': 0.05}. Best is trial 48 with value: 0.7757164745074661.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7695, std_test_score:0.00166 train_set_score:0.8189
folds val/train: [0.7712, 0.767, 0.7683, 0.7701, 0.7712] / [0.8193, 0.8191, 0.8184, 0.8186, 0.8193], mean fold time: 4.07

[I 2024-04-30 15:27:29,006] Trial 55 finished with value: 0.7695424693808656 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 750, 'model__learning_rate': 0.21000000000000002, 'model__max_depth': 10, 'model__num_leaves': 184, 'model__min_gain_to_split': 5.0, 'model__min_data_in_leaf': 1900, 'model__lambda_l1': 70, 'model__lambda_l2': 70, 'model__bagging_fraction': 0.30000000000000004, 'model__feature_fraction': 0.6000000000000001, 'model__max_bin': 200, 'model__drop_rate': 0.0}. Best is trial 48 with value: 0.7757164745074661.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7507, std_test_score:0.02849 train_set_score:0.7659
folds val/train: [0.697, 0.7461, 0.769, 0.7685, 0.773] / [0.703, 0.7608, 0.789, 0.7877, 0.7888], mean fold time: 13.14

[I 2024-04-30 15:28:35,477] Trial 56 finished with value: 0.7507049422160132 and parameters: {'model__class_weight': None, 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 950, 'model__learning_rate': 0.23, 'model__max_depth': 7, 'model__num_leaves': 200, 'model__min_gain_to_split': 3.5, 'model__min_data_in_leaf': 2400, 'model__lambda_l1': 70, 'model__lambda_l2': 60, 'model__bagging_fraction': 0.30000000000000004, 'model__feature_fraction': 0.5, 'model__max_bin': 175, 'model__drop_rate': 0.05}. Best is trial 48 with value: 0.7757164745074661.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7746, std_test_score:0.00158 train_set_score:0.8169
folds val/train: [0.7762, 0.7728, 0.7725, 0.7753, 0.776] / [0.817, 0.8166, 0.8175, 0.8172, 0.8161], mean fold time: 30.07

[I 2024-04-30 15:31:06,637] Trial 57 finished with value: 0.7745768890116641 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 700, 'model__learning_rate': 0.27, 'model__max_depth': 11, 'model__num_leaves': 152, 'model__min_gain_to_split': 7.0, 'model__min_data_in_leaf': 2200, 'model__lambda_l1': 60, 'model__lambda_l2': 80, 'model__bagging_fraction': 0.4, 'model__feature_fraction': 0.6000000000000001, 'model__max_bin': 200, 'model__drop_rate': 0.225}. Best is trial 48 with value: 0.7757164745074661.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7746, std_test_score:0.00167 train_set_score:0.8732
folds val/train: [0.7765, 0.7724, 0.7728, 0.7759, 0.7754] / [0.8739, 0.8716, 0.8734, 0.8759, 0.8714], mean fold time: 22.67

[I 2024-04-30 15:33:00,769] Trial 58 finished with value: 0.7746195085156403 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 650, 'model__learning_rate': 0.3, 'model__max_depth': 9, 'model__num_leaves': 176, 'model__min_gain_to_split': 1.0, 'model__min_data_in_leaf': 1900, 'model__lambda_l1': 80, 'model__lambda_l2': 70, 'model__bagging_fraction': 0.30000000000000004, 'model__feature_fraction': 0.8, 'model__max_bin': 225, 'model__drop_rate': 0.07500000000000001}. Best is trial 48 with value: 0.7757164745074661.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7762, std_test_score:0.00177 train_set_score:0.8635
folds val/train: [0.7787, 0.7741, 0.7742, 0.7772, 0.7766] / [0.8644, 0.8614, 0.8642, 0.865, 0.8623], mean fold time: 17.18

[I 2024-04-30 15:34:27,474] Trial 59 finished with value: 0.7761688739953272 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 850, 'model__learning_rate': 0.18000000000000002, 'model__max_depth': 10, 'model__num_leaves': 128, 'model__min_gain_to_split': 2.5, 'model__min_data_in_leaf': 2100, 'model__lambda_l1': 55, 'model__lambda_l2': 85, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.7, 'model__max_bin': 275, 'model__drop_rate': 0.025}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7724, std_test_score:0.00201 train_set_score:0.8264
folds val/train: [0.7731, 0.7693, 0.771, 0.7741, 0.7747] / [0.8268, 0.8257, 0.8262, 0.827, 0.8264], mean fold time: 4.72

[I 2024-04-30 15:34:51,891] Trial 60 finished with value: 0.772432117479607 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 850, 'model__learning_rate': 0.15000000000000002, 'model__max_depth': 10, 'model__num_leaves': 160, 'model__min_gain_to_split': 2.0, 'model__min_data_in_leaf': 2100, 'model__lambda_l1': 95, 'model__lambda_l2': 90, 'model__bagging_fraction': 0.6000000000000001, 'model__feature_fraction': 0.6000000000000001, 'model__max_bin': 275, 'model__drop_rate': 0.0}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7758, std_test_score:0.00151 train_set_score:0.8548
folds val/train: [0.7772, 0.7737, 0.7743, 0.7766, 0.7773] / [0.8557, 0.854, 0.8546, 0.8558, 0.854], mean fold time: 34.88

[I 2024-04-30 15:37:47,124] Trial 61 finished with value: 0.7758366923779489 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 800, 'model__learning_rate': 0.17, 'model__max_depth': 10, 'model__num_leaves': 128, 'model__min_gain_to_split': 3.0, 'model__min_data_in_leaf': 2400, 'model__lambda_l1': 55, 'model__lambda_l2': 85, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.7, 'model__max_bin': 300, 'model__drop_rate': 0.07500000000000001}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7757, std_test_score:0.00150 train_set_score:0.8537
folds val/train: [0.7772, 0.774, 0.7737, 0.7765, 0.7769] / [0.8545, 0.8529, 0.854, 0.8543, 0.853], mean fold time: 14.51

[I 2024-04-30 15:39:00,514] Trial 62 finished with value: 0.775677769622927 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 750, 'model__learning_rate': 0.17, 'model__max_depth': 9, 'model__num_leaves': 128, 'model__min_gain_to_split': 2.5, 'model__min_data_in_leaf': 2300, 'model__lambda_l1': 65, 'model__lambda_l2': 85, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.7, 'model__max_bin': 300, 'model__drop_rate': 0.025}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7748, std_test_score:0.00159 train_set_score:0.8789
folds val/train: [0.776, 0.7727, 0.7733, 0.7753, 0.7769] / [0.88, 0.8773, 0.8793, 0.8786, 0.8792], mean fold time: 15.22

[I 2024-04-30 15:40:17,443] Trial 63 finished with value: 0.7748341380698116 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 700, 'model__learning_rate': 0.18000000000000002, 'model__max_depth': 9, 'model__num_leaves': 128, 'model__min_gain_to_split': 1.0, 'model__min_data_in_leaf': 2400, 'model__lambda_l1': 65, 'model__lambda_l2': 85, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.7, 'model__max_bin': 300, 'model__drop_rate': 0.025}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7755, std_test_score:0.00156 train_set_score:0.8547
folds val/train: [0.7774, 0.7733, 0.7741, 0.7762, 0.7766] / [0.8559, 0.8537, 0.8547, 0.8554, 0.854], mean fold time: 34.40

[I 2024-04-30 15:43:10,244] Trial 64 finished with value: 0.7755008546468656 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 800, 'model__learning_rate': 0.17, 'model__max_depth': 10, 'model__num_leaves': 136, 'model__min_gain_to_split': 2.5, 'model__min_data_in_leaf': 2000, 'model__lambda_l1': 70, 'model__lambda_l2': 95, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.8, 'model__max_bin': 275, 'model__drop_rate': 0.07500000000000001}. Best is trial 59 with value: 0.7761688739953272.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7721, std_test_score:0.00119 train_set_score:0.8437
folds val/train: [0.7738, 0.7705, 0.771, 0.7729, 0.7723] / [0.8451, 0.8428, 0.8445, 0.8444, 0.8415], mean fold time: 2.43

[I 2024-04-30 15:43:23,223] Trial 65 finished with value: 0.7721005135845032 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'gbdt', 'model__n_estimators': 800, 'model__learning_rate': 0.17, 'model__max_depth': 10, 'model__num_leaves': 136, 'model__min_gain_to_split': 2.5, 'model__min_data_in_leaf': 2000, 'model__lambda_l1': 60, 'model__lambda_l2': 90, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.9000000000000001, 'model__max_bin': 350, 'model__drop_rate': 0.025}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7742, std_test_score:0.00150 train_set_score:0.8669
folds val/train: [0.7756, 0.7718, 0.773, 0.7756, 0.7748] / [0.8677, 0.8665, 0.8666, 0.8665, 0.8672], mean fold time: 41.79

[I 2024-04-30 15:46:52,944] Trial 66 finished with value: 0.7741657203558275 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 900, 'model__learning_rate': 0.12, 'model__max_depth': 9, 'model__num_leaves': 152, 'model__min_gain_to_split': 0.5, 'model__min_data_in_leaf': 1700, 'model__lambda_l1': 80, 'model__lambda_l2': 95, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.8, 'model__max_bin': 325, 'model__drop_rate': 0.07500000000000001}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7739, std_test_score:0.00161 train_set_score:0.8531
folds val/train: [0.7763, 0.7718, 0.7726, 0.7738, 0.7748] / [0.8535, 0.8521, 0.8531, 0.8538, 0.8528], mean fold time: 40.57

[I 2024-04-30 15:50:16,617] Trial 67 finished with value: 0.7738564796154991 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 700, 'model__learning_rate': 0.15000000000000002, 'model__max_depth': 9, 'model__num_leaves': 128, 'model__min_gain_to_split': 2.0, 'model__min_data_in_leaf': 1800, 'model__lambda_l1': 70, 'model__lambda_l2': 95, 'model__bagging_fraction': 0.30000000000000004, 'model__feature_fraction': 0.7, 'model__max_bin': 275, 'model__drop_rate': 0.15000000000000002}. Best is trial 59 with value: 0.7761688739953272.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7281, std_test_score:0.00248 train_set_score:0.7389
folds val/train: [0.7314, 0.7281, 0.7249, 0.7301, 0.7257] / [0.7393, 0.7387, 0.7396, 0.7382, 0.7385], mean fold time: 1.40

[I 2024-04-30 15:50:24,405] Trial 68 finished with value: 0.7280675633752043 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'rf', 'model__n_estimators': 800, 'model__learning_rate': 0.17, 'model__max_depth': 8, 'model__num_leaves': 144, 'model__min_gain_to_split': 3.0, 'model__min_data_in_leaf': 2500, 'model__lambda_l1': 65, 'model__lambda_l2': 85, 'model__bagging_fraction': 0.30000000000000004, 'model__feature_fraction': 0.8, 'model__max_bin': 300, 'model__drop_rate': 0.0}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7756, std_test_score:0.00171 train_set_score:0.8845
folds val/train: [0.7779, 0.7732, 0.7743, 0.7757, 0.7769] / [0.8844, 0.8837, 0.8855, 0.8864, 0.8827], mean fold time: 35.63

[I 2024-04-30 15:53:23,325] Trial 69 finished with value: 0.7756038791367916 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 750, 'model__learning_rate': 0.19, 'model__max_depth': 10, 'model__num_leaves': 176, 'model__min_gain_to_split': 2.5, 'model__min_data_in_leaf': 300, 'model__lambda_l1': 50, 'model__lambda_l2': 105, 'model__bagging_fraction': 1.0, 'model__feature_fraction': 0.7, 'model__max_bin': 325, 'model__drop_rate': 0.07500000000000001}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7752, std_test_score:0.00109 train_set_score:0.8760
folds val/train: [0.7766, 0.7741, 0.7741, 0.7751, 0.7764] / [0.8757, 0.8735, 0.8761, 0.8799, 0.8746], mean fold time: 58.36

[I 2024-04-30 15:58:15,893] Trial 70 finished with value: 0.7752436921563575 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 850, 'model__learning_rate': 0.19, 'model__max_depth': 9, 'model__num_leaves': 184, 'model__min_gain_to_split': 2.5, 'model__min_data_in_leaf': 500, 'model__lambda_l1': 50, 'model__lambda_l2': 105, 'model__bagging_fraction': 0.7, 'model__feature_fraction': 0.8, 'model__max_bin': 325, 'model__drop_rate': 0.65}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7751, std_test_score:0.00152 train_set_score:0.8807
folds val/train: [0.7771, 0.773, 0.7739, 0.7753, 0.7764] / [0.8815, 0.8797, 0.8796, 0.8821, 0.8805], mean fold time: 37.83

[I 2024-04-30 16:01:25,801] Trial 71 finished with value: 0.7751270090588219 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 750, 'model__learning_rate': 0.17, 'model__max_depth': 10, 'model__num_leaves': 168, 'model__min_gain_to_split': 1.5, 'model__min_data_in_leaf': 200, 'model__lambda_l1': 75, 'model__lambda_l2': 90, 'model__bagging_fraction': 1.0, 'model__feature_fraction': 0.7, 'model__max_bin': 300, 'model__drop_rate': 0.07500000000000001}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7751, std_test_score:0.00159 train_set_score:0.8491
folds val/train: [0.7771, 0.7732, 0.7734, 0.7752, 0.7766] / [0.8501, 0.8488, 0.8489, 0.8498, 0.848], mean fold time: 38.68

[I 2024-04-30 16:04:39,998] Trial 72 finished with value: 0.7751061542010573 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 750, 'model__learning_rate': 0.14, 'model__max_depth': 10, 'model__num_leaves': 144, 'model__min_gain_to_split': 4.0, 'model__min_data_in_leaf': 1100, 'model__lambda_l1': 60, 'model__lambda_l2': 105, 'model__bagging_fraction': 0.9000000000000001, 'model__feature_fraction': 0.7, 'model__max_bin': 350, 'model__drop_rate': 0.125}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7755, std_test_score:0.00159 train_set_score:0.8530
folds val/train: [0.7779, 0.7735, 0.774, 0.7756, 0.7764] / [0.8535, 0.851, 0.853, 0.8536, 0.8538], mean fold time: 11.23

[I 2024-04-30 16:05:36,918] Trial 73 finished with value: 0.7754853353009947 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 650, 'model__learning_rate': 0.2, 'model__max_depth': 10, 'model__num_leaves': 120, 'model__min_gain_to_split': 3.5, 'model__min_data_in_leaf': 700, 'model__lambda_l1': 65, 'model__lambda_l2': 95, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.8, 'model__max_bin': 275, 'model__drop_rate': 0.025}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7755, std_test_score:0.00150 train_set_score:0.8678
folds val/train: [0.777, 0.7732, 0.7744, 0.776, 0.777] / [0.8689, 0.8666, 0.8665, 0.8688, 0.8681], mean fold time: 12.65

[I 2024-04-30 16:06:40,961] Trial 74 finished with value: 0.7754914215199437 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 650, 'model__learning_rate': 0.18000000000000002, 'model__max_depth': 10, 'model__num_leaves': 120, 'model__min_gain_to_split': 2.5, 'model__min_data_in_leaf': 800, 'model__lambda_l1': 65, 'model__lambda_l2': 100, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.8, 'model__max_bin': 275, 'model__drop_rate': 0.025}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7742, std_test_score:0.00146 train_set_score:0.9000
folds val/train: [0.7755, 0.772, 0.773, 0.7753, 0.7754] / [0.9005, 0.8974, 0.9001, 0.9022, 0.8999], mean fold time: 12.13

[I 2024-04-30 16:07:42,374] Trial 75 finished with value: 0.7742326340089164 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 550, 'model__learning_rate': 0.2, 'model__max_depth': 10, 'model__num_leaves': 120, 'model__min_gain_to_split': 2.0, 'model__min_data_in_leaf': 700, 'model__lambda_l1': 50, 'model__lambda_l2': 100, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 1.0, 'model__max_bin': 375, 'model__drop_rate': 0.025}. Best is trial 59 with value: 0.7761688739953272.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7691, std_test_score:0.00184 train_set_score:0.7976
folds val/train: [0.7697, 0.7662, 0.7679, 0.7707, 0.7711] / [0.7969, 0.7978, 0.7982, 0.7974, 0.7979], mean fold time: 1.96

[I 2024-04-30 16:07:52,971] Trial 76 finished with value: 0.7691298684460348 and parameters: {'model__class_weight': None, 'model__objective': 'binary', 'model__boosting_type': 'gbdt', 'model__n_estimators': 650, 'model__learning_rate': 0.16, 'model__max_depth': 9, 'model__num_leaves': 120, 'model__min_gain_to_split': 1.5, 'model__min_data_in_leaf': 800, 'model__lambda_l1': 60, 'model__lambda_l2': 105, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.8, 'model__max_bin': 275, 'model__drop_rate': 0.125}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7733, std_test_score:0.00174 train_set_score:0.8230
folds val/train: [0.7762, 0.7712, 0.7721, 0.7729, 0.7742] / [0.8229, 0.8232, 0.8233, 0.8232, 0.8222], mean fold time: 3.74

[I 2024-04-30 16:08:12,472] Trial 77 finished with value: 0.7733325779709974 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 600, 'model__learning_rate': 0.18000000000000002, 'model__max_depth': 5, 'model__num_leaves': 96, 'model__min_gain_to_split': 2.5, 'model__min_data_in_leaf': 400, 'model__lambda_l1': 65, 'model__lambda_l2': 95, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.9000000000000001, 'model__max_bin': 300, 'model__drop_rate': 0.0}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7750, std_test_score:0.00155 train_set_score:0.8402
folds val/train: [0.7772, 0.7734, 0.7735, 0.7745, 0.7765] / [0.84, 0.8395, 0.8406, 0.841, 0.84], mean fold time: 31.77

[I 2024-04-30 16:10:52,163] Trial 78 finished with value: 0.7750098447493975 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 650, 'model__learning_rate': 0.19, 'model__max_depth': 8, 'model__num_leaves': 136, 'model__min_gain_to_split': 3.5, 'model__min_data_in_leaf': 200, 'model__lambda_l1': 80, 'model__lambda_l2': 100, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.8, 'model__max_bin': 325, 'model__drop_rate': 0.17500000000000002}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7756, std_test_score:0.00145 train_set_score:0.8541
folds val/train: [0.7778, 0.7739, 0.7741, 0.7761, 0.7761] / [0.8545, 0.8528, 0.855, 0.855, 0.8533], mean fold time: 33.05

[I 2024-04-30 16:13:38,191] Trial 79 finished with value: 0.7755660895432817 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 700, 'model__learning_rate': 0.21000000000000002, 'model__max_depth': 10, 'model__num_leaves': 112, 'model__min_gain_to_split': 3.0, 'model__min_data_in_leaf': 900, 'model__lambda_l1': 75, 'model__lambda_l2': 15, 'model__bagging_fraction': 0.8, 'model__feature_fraction': 0.9000000000000001, 'model__max_bin': 350, 'model__drop_rate': 0.1}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7733, std_test_score:0.00199 train_set_score:0.8953
folds val/train: [0.7755, 0.7702, 0.772, 0.7739, 0.7751] / [0.8956, 0.8956, 0.8949, 0.8958, 0.8944], mean fold time: 31.29

[I 2024-04-30 16:16:15,458] Trial 80 finished with value: 0.773347375044396 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 700, 'model__learning_rate': 0.21000000000000002, 'model__max_depth': 9, 'model__num_leaves': 256, 'model__min_gain_to_split': 0.5, 'model__min_data_in_leaf': 1300, 'model__lambda_l1': 75, 'model__lambda_l2': 15, 'model__bagging_fraction': 1.0, 'model__feature_fraction': 0.9000000000000001, 'model__max_bin': 325, 'model__drop_rate': 0.07500000000000001}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7758, std_test_score:0.00158 train_set_score:0.8618
folds val/train: [0.7777, 0.7733, 0.7746, 0.7767, 0.7766] / [0.8627, 0.8603, 0.8616, 0.8632, 0.8613], mean fold time: 13.93

[I 2024-04-30 16:17:25,970] Trial 81 finished with value: 0.775779859991285 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 650, 'model__learning_rate': 0.18000000000000002, 'model__max_depth': 10, 'model__num_leaves': 112, 'model__min_gain_to_split': 3.0, 'model__min_data_in_leaf': 600, 'model__lambda_l1': 70, 'model__lambda_l2': 95, 'model__bagging_fraction': 0.8, 'model__feature_fraction': 1.0, 'model__max_bin': 400, 'model__drop_rate': 0.025}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7745, std_test_score:0.00138 train_set_score:0.8532
folds val/train: [0.7765, 0.7727, 0.7733, 0.7749, 0.7753] / [0.8536, 0.8521, 0.853, 0.8545, 0.853], mean fold time: 23.42

[I 2024-04-30 16:19:23,879] Trial 82 finished with value: 0.7745487594810218 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 550, 'model__learning_rate': 0.18000000000000002, 'model__max_depth': 10, 'model__num_leaves': 112, 'model__min_gain_to_split': 3.0, 'model__min_data_in_leaf': 900, 'model__lambda_l1': 85, 'model__lambda_l2': 25, 'model__bagging_fraction': 0.8, 'model__feature_fraction': 1.0, 'model__max_bin': 400, 'model__drop_rate': 0.1}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7727, std_test_score:0.00172 train_set_score:0.8117
folds val/train: [0.7746, 0.7705, 0.7709, 0.7731, 0.7743] / [0.8114, 0.8113, 0.8121, 0.8122, 0.8116], mean fold time: 36.42

[I 2024-04-30 16:22:26,786] Trial 83 finished with value: 0.7727030405930293 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 800, 'model__learning_rate': 0.16, 'model__max_depth': 10, 'model__num_leaves': 96, 'model__min_gain_to_split': 15.0, 'model__min_data_in_leaf': 500, 'model__lambda_l1': 70, 'model__lambda_l2': 5, 'model__bagging_fraction': 0.9000000000000001, 'model__feature_fraction': 1.0, 'model__max_bin': 375, 'model__drop_rate': 0.15000000000000002}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7749, std_test_score:0.00199 train_set_score:0.8554
folds val/train: [0.7771, 0.7724, 0.7726, 0.7761, 0.7762] / [0.8547, 0.8545, 0.8561, 0.8573, 0.8542], mean fold time: 63.92

[I 2024-04-30 16:27:47,211] Trial 84 finished with value: 0.7748771629162474 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 950, 'model__learning_rate': 0.19, 'model__max_depth': 10, 'model__num_leaves': 128, 'model__min_gain_to_split': 2.5, 'model__min_data_in_leaf': 600, 'model__lambda_l1': 75, 'model__lambda_l2': 85, 'model__bagging_fraction': 0.7, 'model__feature_fraction': 1.0, 'model__max_bin': 350, 'model__drop_rate': 0.375}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7739, std_test_score:0.00150 train_set_score:0.8961
folds val/train: [0.7762, 0.7717, 0.773, 0.7741, 0.7746] / [0.8969, 0.8939, 0.8964, 0.8972, 0.8962], mean fold time: 47.56

[I 2024-04-30 16:31:45,930] Trial 85 finished with value: 0.7738991785550662 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 700, 'model__learning_rate': 0.13, 'model__max_depth': 10, 'model__num_leaves': 240, 'model__min_gain_to_split': 1.5, 'model__min_data_in_leaf': 300, 'model__lambda_l1': 60, 'model__lambda_l2': 110, 'model__bagging_fraction': 0.8, 'model__feature_fraction': 0.9000000000000001, 'model__max_bin': 350, 'model__drop_rate': 0.1}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7750, std_test_score:0.00159 train_set_score:0.8539
folds val/train: [0.777, 0.7724, 0.7742, 0.775, 0.7762] / [0.8547, 0.8525, 0.8538, 0.8549, 0.8535], mean fold time: 11.96

[I 2024-04-30 16:32:46,558] Trial 86 finished with value: 0.7749659726611313 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 600, 'model__learning_rate': 0.15000000000000002, 'model__max_depth': 9, 'model__num_leaves': 88, 'model__min_gain_to_split': 3.0, 'model__min_data_in_leaf': 900, 'model__lambda_l1': 80, 'model__lambda_l2': 0, 'model__bagging_fraction': 0.8, 'model__feature_fraction': 0.9000000000000001, 'model__max_bin': 375, 'model__drop_rate': 0.025}. Best is trial 59 with value: 0.7761688739953272.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7367, std_test_score:0.00301 train_set_score:0.7580
folds val/train: [0.7354, 0.7384, 0.7314, 0.7399, 0.7381] / [0.7524, 0.7615, 0.754, 0.7611, 0.7611], mean fold time: 1.76

[I 2024-04-30 16:32:56,192] Trial 87 finished with value: 0.7366615449617313 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'rf', 'model__n_estimators': 850, 'model__learning_rate': 0.21000000000000002, 'model__max_depth': 10, 'model__num_leaves': 112, 'model__min_gain_to_split': 2.0, 'model__min_data_in_leaf': 400, 'model__lambda_l1': 65, 'model__lambda_l2': 90, 'model__bagging_fraction': 1.0, 'model__feature_fraction': 0.7, 'model__max_bin': 275, 'model__drop_rate': 0.07500000000000001}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7716, std_test_score:0.00174 train_set_score:0.7955
folds val/train: [0.7732, 0.7695, 0.7703, 0.7712, 0.774] / [0.7954, 0.7958, 0.7961, 0.7954, 0.795], mean fold time: 22.75

[I 2024-04-30 16:34:50,902] Trial 88 finished with value: 0.7716211150804543 and parameters: {'model__class_weight': None, 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 700, 'model__learning_rate': 0.22, 'model__max_depth': 10, 'model__num_leaves': 136, 'model__min_gain_to_split': 3.5, 'model__min_data_in_leaf': 1100, 'model__lambda_l1': 55, 'model__lambda_l2': 100, 'model__bagging_fraction': 0.6000000000000001, 'model__feature_fraction': 0.9000000000000001, 'model__max_bin': 300, 'model__drop_rate': 0.2}. Best is trial 59 with value: 0.7761688739953272.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7695, std_test_score:0.00191 train_set_score:0.8630
folds val/train: [0.771, 0.7662, 0.7685, 0.7704, 0.7713] / [0.864, 0.8648, 0.8627, 0.8615, 0.8619], mean fold time: 2.26

[I 2024-04-30 16:35:02,995] Trial 89 finished with value: 0.7694688745259566 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'gbdt', 'model__n_estimators': 800, 'model__learning_rate': 0.18000000000000002, 'model__max_depth': 10, 'model__num_leaves': 128, 'model__min_gain_to_split': 4.0, 'model__min_data_in_leaf': 100, 'model__lambda_l1': 40, 'model__lambda_l2': 105, 'model__bagging_fraction': 0.9000000000000001, 'model__feature_fraction': 1.0, 'model__max_bin': 250, 'model__drop_rate': 0.025}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7724, std_test_score:0.00166 train_set_score:0.8095
folds val/train: [0.7744, 0.7704, 0.7705, 0.7727, 0.7739] / [0.8097, 0.8095, 0.8093, 0.8096, 0.8097], mean fold time: 27.54

[I 2024-04-30 16:37:21,487] Trial 90 finished with value: 0.7723829604261204 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 650, 'model__learning_rate': 0.17, 'model__max_depth': 9, 'model__num_leaves': 144, 'model__min_gain_to_split': 12.0, 'model__min_data_in_leaf': 600, 'model__lambda_l1': 90, 'model__lambda_l2': 95, 'model__bagging_fraction': 0.9000000000000001, 'model__feature_fraction': 0.8, 'model__max_bin': 450, 'model__drop_rate': 0.15000000000000002}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7757, std_test_score:0.00151 train_set_score:0.8547
folds val/train: [0.7778, 0.7739, 0.7743, 0.7758, 0.777] / [0.8551, 0.853, 0.8553, 0.856, 0.8542], mean fold time: 11.27

[I 2024-04-30 16:38:18,645] Trial 91 finished with value: 0.7757482290903817 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 600, 'model__learning_rate': 0.2, 'model__max_depth': 10, 'model__num_leaves': 120, 'model__min_gain_to_split': 3.5, 'model__min_data_in_leaf': 800, 'model__lambda_l1': 65, 'model__lambda_l2': 40, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.8, 'model__max_bin': 325, 'model__drop_rate': 0.025}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7709, std_test_score:0.00190 train_set_score:0.8454
folds val/train: [0.7735, 0.7679, 0.7699, 0.7713, 0.7717] / [0.8465, 0.846, 0.8464, 0.8436, 0.8444], mean fold time: 4.43

[I 2024-04-30 16:38:41,623] Trial 92 finished with value: 0.770868189277009 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 500, 'model__learning_rate': 0.2, 'model__max_depth': 10, 'model__num_leaves': 104, 'model__min_gain_to_split': 2.5, 'model__min_data_in_leaf': 900, 'model__lambda_l1': 70, 'model__lambda_l2': 35, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.7, 'model__max_bin': 325, 'model__drop_rate': 0.0}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7744, std_test_score:0.00144 train_set_score:0.8848
folds val/train: [0.7765, 0.7725, 0.7731, 0.775, 0.7749] / [0.8848, 0.8833, 0.8847, 0.8862, 0.885], mean fold time: 25.57

[I 2024-04-30 16:40:50,415] Trial 93 finished with value: 0.7743807498191898 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 550, 'model__learning_rate': 0.16, 'model__max_depth': 10, 'model__num_leaves': 120, 'model__min_gain_to_split': 3.0, 'model__min_data_in_leaf': 800, 'model__lambda_l1': 45, 'model__lambda_l2': 15, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.8, 'model__max_bin': 350, 'model__drop_rate': 0.07500000000000001}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7744, std_test_score:0.00151 train_set_score:0.8847
folds val/train: [0.7763, 0.7724, 0.7729, 0.775, 0.7756] / [0.8855, 0.8835, 0.885, 0.8852, 0.8841], mean fold time: 34.76

[I 2024-04-30 16:43:45,044] Trial 94 finished with value: 0.774422820088884 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 600, 'model__learning_rate': 0.19, 'model__max_depth': 10, 'model__num_leaves': 112, 'model__min_gain_to_split': 2.0, 'model__min_data_in_leaf': 600, 'model__lambda_l1': 65, 'model__lambda_l2': 20, 'model__bagging_fraction': 1.0, 'model__feature_fraction': 0.9000000000000001, 'model__max_bin': 475, 'model__drop_rate': 0.1}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7730, std_test_score:0.00123 train_set_score:0.9135
folds val/train: [0.7739, 0.7714, 0.7716, 0.7738, 0.7743] / [0.9156, 0.9132, 0.913, 0.9128, 0.9128], mean fold time: 28.02

[I 2024-04-30 16:46:05,926] Trial 95 finished with value: 0.7730060317441305 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 700, 'model__learning_rate': 0.17, 'model__max_depth': 9, 'model__num_leaves': 136, 'model__min_gain_to_split': 0.5, 'model__min_data_in_leaf': 1000, 'model__lambda_l1': 50, 'model__lambda_l2': 40, 'model__bagging_fraction': 0.7, 'model__feature_fraction': 0.7, 'model__max_bin': 300, 'model__drop_rate': 0.05}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7754, std_test_score:0.00126 train_set_score:0.8415
folds val/train: [0.7769, 0.7733, 0.7749, 0.7764, 0.7756] / [0.842, 0.8405, 0.8433, 0.8415, 0.8401], mean fold time: 37.61

[I 2024-04-30 16:49:14,808] Trial 96 finished with value: 0.7754047018714463 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 750, 'model__learning_rate': 0.2, 'model__max_depth': 10, 'model__num_leaves': 128, 'model__min_gain_to_split': 4.0, 'model__min_data_in_leaf': 800, 'model__lambda_l1': 70, 'model__lambda_l2': 100, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.8, 'model__max_bin': 325, 'model__drop_rate': 0.125}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7702, std_test_score:0.00137 train_set_score:0.8388
folds val/train: [0.7723, 0.7681, 0.77, 0.7709, 0.7697] / [0.8404, 0.8381, 0.8393, 0.8388, 0.8376], mean fold time: 3.77

[I 2024-04-30 16:49:34,456] Trial 97 finished with value: 0.7702235699309032 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 650, 'model__learning_rate': 0.22, 'model__max_depth': 10, 'model__num_leaves': 96, 'model__min_gain_to_split': 3.5, 'model__min_data_in_leaf': 1200, 'model__lambda_l1': 60, 'model__lambda_l2': 85, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.8, 'model__max_bin': 400, 'model__drop_rate': 0.0}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7741, std_test_score:0.00162 train_set_score:0.8647
folds val/train: [0.7758, 0.7717, 0.7726, 0.7749, 0.7753] / [0.8658, 0.8636, 0.8637, 0.8658, 0.8646], mean fold time: 13.45

[I 2024-04-30 16:50:42,559] Trial 98 finished with value: 0.7740746849233707 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 450, 'model__learning_rate': 0.18000000000000002, 'model__max_depth': 11, 'model__num_leaves': 152, 'model__min_gain_to_split': 2.5, 'model__min_data_in_leaf': 500, 'model__lambda_l1': 75, 'model__lambda_l2': 50, 'model__bagging_fraction': 0.6000000000000001, 'model__feature_fraction': 0.7, 'model__max_bin': 275, 'model__drop_rate': 0.05}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7758, std_test_score:0.00124 train_set_score:0.8553
folds val/train: [0.7776, 0.7744, 0.7747, 0.7768, 0.7755] / [0.8556, 0.8533, 0.8544, 0.8578, 0.8554], mean fold time: 16.60

[I 2024-04-30 16:52:06,575] Trial 99 finished with value: 0.7758031999945074 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 800, 'model__learning_rate': 0.17, 'model__max_depth': 10, 'model__num_leaves': 112, 'model__min_gain_to_split': 3.0, 'model__min_data_in_leaf': 2400, 'model__lambda_l1': 55, 'model__lambda_l2': 90, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.7, 'model__max_bin': 300, 'model__drop_rate': 0.025}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7757, std_test_score:0.00150 train_set_score:0.8401
folds val/train: [0.7778, 0.7742, 0.7738, 0.7764, 0.7765] / [0.8404, 0.8389, 0.8402, 0.841, 0.8401], mean fold time: 35.98

[I 2024-04-30 16:55:07,292] Trial 100 finished with value: 0.7757316271452567 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 850, 'model__learning_rate': 0.14, 'model__max_depth': 9, 'model__num_leaves': 112, 'model__min_gain_to_split': 4.5, 'model__min_data_in_leaf': 2500, 'model__lambda_l1': 55, 'model__lambda_l2': 90, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.7, 'model__max_bin': 300, 'model__drop_rate': 0.07500000000000001}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7754, std_test_score:0.00146 train_set_score:0.8384
folds val/train: [0.7771, 0.774, 0.7733, 0.7762, 0.7764] / [0.8399, 0.8371, 0.8383, 0.8395, 0.8374], mean fold time: 33.75

[I 2024-04-30 16:57:56,835] Trial 101 finished with value: 0.7754186394673495 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 850, 'model__learning_rate': 0.14, 'model__max_depth': 8, 'model__num_leaves': 112, 'model__min_gain_to_split': 4.5, 'model__min_data_in_leaf': 2400, 'model__lambda_l1': 55, 'model__lambda_l2': 90, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.7, 'model__max_bin': 300, 'model__drop_rate': 0.07500000000000001}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7745, std_test_score:0.00194 train_set_score:0.8594
folds val/train: [0.7772, 0.773, 0.7716, 0.7755, 0.775] / [0.8601, 0.8572, 0.8605, 0.8614, 0.8578], mean fold time: 52.88

[I 2024-04-30 17:02:22,041] Trial 102 finished with value: 0.7744561172372163 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 900, 'model__learning_rate': 0.15000000000000002, 'model__max_depth': 9, 'model__num_leaves': 104, 'model__min_gain_to_split': 3.0, 'model__min_data_in_leaf': 2500, 'model__lambda_l1': 50, 'model__lambda_l2': 85, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.7, 'model__max_bin': 300, 'model__drop_rate': 0.5}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7761, std_test_score:0.00172 train_set_score:0.8501
folds val/train: [0.7781, 0.7739, 0.7742, 0.7772, 0.7771] / [0.8526, 0.8474, 0.8496, 0.8511, 0.85], mean fold time: 35.80

[I 2024-04-30 17:05:21,829] Trial 103 finished with value: 0.7760978932333764 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 800, 'model__learning_rate': 0.16, 'model__max_depth': 9, 'model__num_leaves': 128, 'model__min_gain_to_split': 3.0, 'model__min_data_in_leaf': 2600, 'model__lambda_l1': 55, 'model__lambda_l2': 80, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.7, 'model__max_bin': 325, 'model__drop_rate': 0.1}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7750, std_test_score:0.00132 train_set_score:0.8458
folds val/train: [0.7771, 0.7735, 0.7738, 0.7748, 0.7756] / [0.8461, 0.844, 0.8456, 0.8477, 0.8455], mean fold time: 43.05

[I 2024-04-30 17:08:57,900] Trial 104 finished with value: 0.7749804267188923 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 850, 'model__learning_rate': 0.12, 'model__max_depth': 7, 'model__num_leaves': 104, 'model__min_gain_to_split': 3.5, 'model__min_data_in_leaf': 2600, 'model__lambda_l1': 55, 'model__lambda_l2': 75, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.7, 'model__max_bin': 325, 'model__drop_rate': 0.925}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7755, std_test_score:0.00135 train_set_score:0.8378
folds val/train: [0.7769, 0.7736, 0.7748, 0.7751, 0.7771] / [0.8372, 0.8363, 0.8387, 0.8393, 0.8376], mean fold time: 36.23

[I 2024-04-30 17:11:59,896] Trial 105 finished with value: 0.7754972846803143 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 800, 'model__learning_rate': 0.13, 'model__max_depth': 8, 'model__num_leaves': 112, 'model__min_gain_to_split': 4.5, 'model__min_data_in_leaf': 2600, 'model__lambda_l1': 50, 'model__lambda_l2': 80, 'model__bagging_fraction': 0.30000000000000004, 'model__feature_fraction': 0.6000000000000001, 'model__max_bin': 350, 'model__drop_rate': 0.125}. Best is trial 59 with value: 0.7761688739953272.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7310, std_test_score:0.00175 train_set_score:0.7426
folds val/train: [0.7319, 0.7297, 0.7328, 0.7322, 0.7282] / [0.7403, 0.743, 0.7484, 0.7408, 0.7404], mean fold time: 1.32

[I 2024-04-30 17:12:07,323] Trial 106 finished with value: 0.7309608703167031 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'rf', 'model__n_estimators': 750, 'model__learning_rate': 0.16, 'model__max_depth': 9, 'model__num_leaves': 88, 'model__min_gain_to_split': 4.0, 'model__min_data_in_leaf': 2300, 'model__lambda_l1': 60, 'model__lambda_l2': 80, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.7, 'model__max_bin': 325, 'model__drop_rate': 0.025}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7572, std_test_score:0.00175 train_set_score:0.7737
folds val/train: [0.7571, 0.7548, 0.7558, 0.7596, 0.7585] / [0.7723, 0.775, 0.7743, 0.774, 0.7729], mean fold time: 2.48

[I 2024-04-30 17:12:20,567] Trial 107 finished with value: 0.7571678211567875 and parameters: {'model__class_weight': None, 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 100, 'model__learning_rate': 0.13, 'model__max_depth': 9, 'model__num_leaves': 128, 'model__min_gain_to_split': 3.0, 'model__min_data_in_leaf': 2400, 'model__lambda_l1': 45, 'model__lambda_l2': 90, 'model__bagging_fraction': 0.7, 'model__feature_fraction': 0.7, 'model__max_bin': 350, 'model__drop_rate': 0.1}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7753, std_test_score:0.00136 train_set_score:0.8878
folds val/train: [0.7777, 0.774, 0.7739, 0.7756, 0.7753] / [0.8879, 0.8869, 0.8887, 0.8885, 0.8869], mean fold time: 30.34

[I 2024-04-30 17:14:53,087] Trial 108 finished with value: 0.7752973754291087 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 900, 'model__learning_rate': 0.17, 'model__max_depth': 9, 'model__num_leaves': 192, 'model__min_gain_to_split': 1.0, 'model__min_data_in_leaf': 2100, 'model__lambda_l1': 55, 'model__lambda_l2': 85, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.6000000000000001, 'model__max_bin': 300, 'model__drop_rate': 0.05}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7733, std_test_score:0.00148 train_set_score:0.8433
folds val/train: [0.7753, 0.771, 0.7732, 0.7743, 0.7728] / [0.8429, 0.8438, 0.8432, 0.843, 0.8436], mean fold time: 4.43

[I 2024-04-30 17:15:16,058] Trial 109 finished with value: 0.77331191667464 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 750, 'model__learning_rate': 0.15000000000000002, 'model__max_depth': 10, 'model__num_leaves': 120, 'model__min_gain_to_split': 2.0, 'model__min_data_in_leaf': 2500, 'model__lambda_l1': 60, 'model__lambda_l2': 75, 'model__bagging_fraction': 0.8, 'model__feature_fraction': 0.7, 'model__max_bin': 325, 'model__drop_rate': 0.0}. Best is trial 59 with value: 0.7761688739953272.

Fold: Tuning: n_train=246008, eval_set=61503
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7716, std_test_score:0.00161 train_set_score:0.8491
folds val/train: [0.7726, 0.77, 0.77, 0.7712, 0.7741] / [0.8445, 0.8435, 0.8537, 0.8467, 0.8568], mean fold time: 2.29

[I 2024-04-30 17:15:28,304] Trial 110 finished with value: 0.7715890545145191 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'gbdt', 'model__n_estimators': 850, 'model__learning_rate': 0.21000000000000002, 'model__max_depth': 9, 'model__num_leaves': 144, 'model__min_gain_to_split': 1.5, 'model__min_data_in_leaf': 2900, 'model__lambda_l1': 50, 'model__lambda_l2': 45, 'model__bagging_fraction': 0.30000000000000004, 'model__feature_fraction': 0.7, 'model__max_bin': 375, 'model__drop_rate': 0.15000000000000002}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7751, std_test_score:0.00142 train_set_score:0.8569
folds val/train: [0.7768, 0.7733, 0.7737, 0.7753, 0.7765] / [0.8571, 0.8553, 0.8578, 0.8579, 0.8566], mean fold time: 33.26

[I 2024-04-30 17:18:15,411] Trial 111 finished with value: 0.775113320499737 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 800, 'model__learning_rate': 0.14, 'model__max_depth': 10, 'model__num_leaves': 136, 'model__min_gain_to_split': 3.5, 'model__min_data_in_leaf': 2300, 'model__lambda_l1': 45, 'model__lambda_l2': 95, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.7, 'model__max_bin': 275, 'model__drop_rate': 0.07500000000000001}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7756, std_test_score:0.00156 train_set_score:0.8568
folds val/train: [0.777, 0.7736, 0.7738, 0.7765, 0.777] / [0.8573, 0.8556, 0.8565, 0.8583, 0.8562], mean fold time: 22.03

[I 2024-04-30 17:20:06,343] Trial 112 finished with value: 0.7755704469706053 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 800, 'model__learning_rate': 0.19, 'model__max_depth': 10, 'model__num_leaves': 120, 'model__min_gain_to_split': 3.0, 'model__min_data_in_leaf': 2200, 'model__lambda_l1': 55, 'model__lambda_l2': 10, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.7, 'model__max_bin': 250, 'model__drop_rate': 0.05}. Best is trial 59 with value: 0.7761688739953272.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7765, std_test_score:0.00146 train_set_score:0.8546
folds val/train: [0.7783, 0.7756, 0.7742, 0.7776, 0.7771] / [0.8552, 0.8534, 0.8547, 0.8556, 0.8543], mean fold time: 14.86

[I 2024-04-30 17:21:21,449] Trial 113 finished with value: 0.7765413336056665 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 800, 'model__learning_rate': 0.19, 'model__max_depth': 10, 'model__num_leaves': 112, 'model__min_gain_to_split': 3.0, 'model__min_data_in_leaf': 2400, 'model__lambda_l1': 55, 'model__lambda_l2': 10, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.6000000000000001, 'model__max_bin': 250, 'model__drop_rate': 0.025}. Best is trial 113 with value: 0.7765413336056665.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7756, std_test_score:0.00150 train_set_score:0.8426
folds val/train: [0.7774, 0.774, 0.7737, 0.7762, 0.7769] / [0.8434, 0.841, 0.8424, 0.8439, 0.8425], mean fold time: 22.12

[I 2024-04-30 17:23:12,858] Trial 114 finished with value: 0.7756165234444479 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 800, 'model__learning_rate': 0.18000000000000002, 'model__max_depth': 11, 'model__num_leaves': 128, 'model__min_gain_to_split': 4.0, 'model__min_data_in_leaf': 2600, 'model__lambda_l1': 55, 'model__lambda_l2': 10, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.6000000000000001, 'model__max_bin': 250, 'model__drop_rate': 0.05}. Best is trial 113 with value: 0.7765413336056665.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7761, std_test_score:0.00135 train_set_score:0.8373
folds val/train: [0.7781, 0.7744, 0.7749, 0.7765, 0.7768] / [0.8377, 0.8358, 0.8372, 0.8385, 0.8371], mean fold time: 18.33

[I 2024-04-30 17:24:45,328] Trial 115 finished with value: 0.7761332450237166 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 950, 'model__learning_rate': 0.16, 'model__max_depth': 11, 'model__num_leaves': 128, 'model__min_gain_to_split': 4.0, 'model__min_data_in_leaf': 2400, 'model__lambda_l1': 60, 'model__lambda_l2': 10, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.5, 'model__max_bin': 225, 'model__drop_rate': 0.025}. Best is trial 113 with value: 0.7765413336056665.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7759, std_test_score:0.00154 train_set_score:0.8366
folds val/train: [0.7781, 0.7741, 0.7742, 0.7761, 0.7769] / [0.837, 0.8361, 0.8361, 0.838, 0.8358], mean fold time: 19.93

[I 2024-04-30 17:26:25,792] Trial 116 finished with value: 0.7759056876493742 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 1000, 'model__learning_rate': 0.16, 'model__max_depth': 11, 'model__num_leaves': 128, 'model__min_gain_to_split': 4.0, 'model__min_data_in_leaf': 2600, 'model__lambda_l1': 60, 'model__lambda_l2': 10, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.5, 'model__max_bin': 250, 'model__drop_rate': 0.025}. Best is trial 113 with value: 0.7765413336056665.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7756, std_test_score:0.00146 train_set_score:0.8291
folds val/train: [0.7778, 0.7741, 0.774, 0.7756, 0.7765] / [0.8294, 0.8283, 0.8287, 0.8297, 0.8295], mean fold time: 17.78

[I 2024-04-30 17:27:55,523] Trial 117 finished with value: 0.7756190139885522 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 1000, 'model__learning_rate': 0.16, 'model__max_depth': 11, 'model__num_leaves': 96, 'model__min_gain_to_split': 4.5, 'model__min_data_in_leaf': 2700, 'model__lambda_l1': 60, 'model__lambda_l2': 10, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.4, 'model__max_bin': 225, 'model__drop_rate': 0.025}. Best is trial 113 with value: 0.7765413336056665.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7718, std_test_score:0.00178 train_set_score:0.8200
folds val/train: [0.7741, 0.769, 0.7708, 0.7729, 0.7723] / [0.82, 0.82, 0.8212, 0.8194, 0.8196], mean fold time: 5.72

[I 2024-04-30 17:28:24,986] Trial 118 finished with value: 0.771828010043889 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 950, 'model__learning_rate': 0.16, 'model__max_depth': 11, 'model__num_leaves': 128, 'model__min_gain_to_split': 4.5, 'model__min_data_in_leaf': 2500, 'model__lambda_l1': 65, 'model__lambda_l2': 5, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.5, 'model__max_bin': 250, 'model__drop_rate': 0.0}. Best is trial 113 with value: 0.7765413336056665.
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246008, eval_set=61503

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Fold: Tuning: n_train=246009, eval_set=61502
Tune: val_score:0.7761, std_test_score:0.00144 train_set_score:0.8336
folds val/train: [0.7776, 0.7739, 0.775, 0.7766, 0.7773] / [0.8335, 0.8328, 0.8341, 0.8345, 0.8334], mean fold time: 18.60

[I 2024-04-30 17:29:58,807] Trial 119 finished with value: 0.7760703157434982 and parameters: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 1000, 'model__learning_rate': 0.15000000000000002, 'model__max_depth': 11, 'model__num_leaves': 104, 'model__min_gain_to_split': 4.0, 'model__min_data_in_leaf': 2400, 'model__lambda_l1': 60, 'model__lambda_l2': 25, 'model__bagging_fraction': 0.30000000000000004, 'model__feature_fraction': 0.4, 'model__max_bin': 225, 'model__drop_rate': 0.025}. Best is trial 113 with value: 0.7765413336056665.

 -- -- --
 TUNING RESULT: 
 LGBM_Dart_AUC_NEW: auc = 0.7765413336056665 params: {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 800, 'model__learning_rate': 0.19, 'model__max_depth': 10, 'model__num_leaves': 112, 'model__min_gain_to_split': 3.0, 'model__min_data_in_leaf': 2400, 'model__lambda_l1': 55, 'model__lambda_l2': 10, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.6000000000000001, 'model__max_bin': 250, 'model__drop_rate': 0.025}
 -- --

LGBM_Dart_AUC_NEW Fit, total time:11958.6

'LGBM_Dart_AUC_NEW'

	Trial Number	Mean Test Score (AUC)	mean_test_score	mean_train_score	std_test_score	macro_f1	micro_f1	f1_target1	precision_target1	recall_target1	log_loss	pr_auc	mean_fold_time	Parameters
113	113	0.776541	0.776541	0.854625	0.001461	0.568715	0.744595	0.293298	0.188829	0.656516	0.521725	0.266816	14.861349	{'model__boosting_type': 'dart', 'model__objec...
59	59	0.776169	0.776169	0.863464	0.001774	0.571076	0.748920	0.294886	0.190670	0.650352	0.515669	0.265697	17.182891	{'model__boosting_type': 'dart', 'model__objec...
115	115	0.776133	0.776133	0.837253	0.001353	0.563986	0.735886	0.290214	0.185310	0.668842	0.533418	0.265379	18.331637	{'model__boosting_type': 'dart', 'model__objec...
103	103	0.776098	0.776098	0.850141	0.001724	0.567537	0.742464	0.292491	0.187923	0.659416	0.524208	0.266197	35.797750	{'model__boosting_type': 'dart', 'model__objec...
119	119	0.776070	0.776070	0.833643	0.001436	0.562364	0.733788	0.288463	0.183916	0.668439	0.535529	0.265608	18.596235	{'model__boosting_type': 'dart', 'model__objec...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
68	68	0.728068	0.728068	0.738853	0.002483	0.522548	0.677790	0.250297	0.154093	0.666264	0.614381	0.201428	1.399441	{'model__boosting_type': 'rf', 'model__objecti...
2	2	0.726644	0.726644	0.735570	0.002767	0.478969	0.919271	0.000000	0.000000	0.000000	0.260398	0.204315	1.246094	{'model__boosting_type': 'rf', 'model__objecti...
45	45	0.725722	0.725722	0.737205	0.001131	0.520557	0.675839	0.247704	0.152405	0.661067	0.613808	0.194422	1.432226	{'model__boosting_type': 'rf', 'model__objecti...
9	9	0.725698	0.725698	0.735554	0.002000	0.525812	0.914384	0.096562	0.325921	0.056677	0.260996	0.197951	2.336899	{'model__boosting_type': 'rf', 'model__objecti...
0	0	0.724759	0.724759	0.730835	0.002090	0.478969	0.919271	0.000000	0.000000	0.000000	0.258624	0.199263	1.240461	{'model__boosting_type': 'rf', 'model__objecti...

120 rows × 14 columns

dict_keys(['LGBM_Dart_AUC_NEW'])

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\entityset\entityset.py:1914: UserWarning: index SK_BUREAU_ID not found in dataframe, creating new integer column
  warnings.warn(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\computational_backends\feature_set_calculator.py:828: FutureWarning: The provided callable <function min at 0x000001D77E574400> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
  ).agg(to_agg)
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\computational_backends\feature_set_calculator.py:828: FutureWarning: The provided callable <function max at 0x000001D77E5742C0> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
  ).agg(to_agg)
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\computational_backends\feature_set_calculator.py:828: FutureWarning: The provided callable <function mean at 0x000001D77E574CC0> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
  ).agg(to_agg)

Appending previous history
Full DS size: 307511

	TARGET	NameContractType	CodeGender	FlagOwnCar	FlagOwnRealty	CntChildren	AmtIncomeTotal	AmtCredit	AmtAnnuity	AmtGoodsPrice	...	PrevCodeRejectReasonHcCount	PrevCodeRejectReasonLimitCount	PrevCodeRejectReasonScoCount	PrevCodeRejectReasonScofrCount	PrevCodeRejectReasonSystemCount	PrevCodeRejectReasonVerifCount	PrevCodeRejectReasonXapCount	PrevCodeRejectReasonXnaCount	PrevLastLoanNflagInsuredOnApproval	PrevAvgNflagInsuredOnApproval
0	1	Cash loans	M	False	True	0	202500.0	406597.5	24700.5	351000.0	...	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.000000
1	0	Cash loans	F	False	False	0	270000.0	1293502.5	35698.5	1129500.0	...	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	1.0	0.666667
2	0	Revolving loans	M	True	True	0	67500.0	135000.0	6750.0	135000.0	...	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.000000
3	0	Cash loans	F	False	True	0	135000.0	312682.5	29686.5	297000.0	...	0.0	1.0	0.0	0.0	0.0	0.0	8.0	0.0	NaN	0.000000
4	0	Cash loans	M	False	True	0	121500.0	513000.0	21865.5	513000.0	...	0.0	0.0	0.0	0.0	0.0	0.0	6.0	0.0	1.0	0.600000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
307506	0	Cash loans	M	False	False	0	157500.0	254700.0	27558.0	225000.0	...	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.000000
307507	0	Cash loans	F	False	True	0	72000.0	269550.0	12001.5	225000.0	...	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	1.000000
307508	0	Cash loans	F	False	True	0	153000.0	677664.0	29979.0	585000.0	...	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	0.500000
307509	1	Cash loans	F	False	True	0	171000.0	370107.0	20205.0	319500.0	...	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	1.0	0.500000
307510	0	Cash loans	F	False	False	0	157500.0	675000.0	49117.5	675000.0	...	2.0	0.0	0.0	0.0	0.0	0.0	6.0	0.0	NaN	0.333333

307511 rows × 227 columns

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\entityset\entityset.py:1914: UserWarning: index SK_BUREAU_ID not found in dataframe, creating new integer column
  warnings.warn(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\computational_backends\feature_set_calculator.py:828: FutureWarning: The provided callable <function min at 0x000001D77E574400> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
  ).agg(to_agg)
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\computational_backends\feature_set_calculator.py:828: FutureWarning: The provided callable <function max at 0x000001D77E5742C0> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
  ).agg(to_agg)
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\computational_backends\feature_set_calculator.py:828: FutureWarning: The provided callable <function mean at 0x000001D77E574CC0> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
  ).agg(to_agg)

Appending previous history
Full DS size: 307511
Full DS size: 307511

	TARGET	NameContractType	CodeGender	FlagOwnCar	FlagOwnRealty	CntChildren	AmtIncomeTotal	AmtCredit	AmtAnnuity	AmtGoodsPrice	...	PrevCodeRejectReasonHcCount	PrevCodeRejectReasonLimitCount	PrevCodeRejectReasonScoCount	PrevCodeRejectReasonScofrCount	PrevCodeRejectReasonSystemCount	PrevCodeRejectReasonVerifCount	PrevCodeRejectReasonXapCount	PrevCodeRejectReasonXnaCount	PrevLastLoanNflagInsuredOnApproval	PrevAvgNflagInsuredOnApproval
0	1	Cash loans	M	False	True	0	202500.0	406597.5	24700.5	351000.0	...	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.000000
1	0	Cash loans	F	False	False	0	270000.0	1293502.5	35698.5	1129500.0	...	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	1.0	0.666667
2	0	Revolving loans	M	True	True	0	67500.0	135000.0	6750.0	135000.0	...	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.000000
3	0	Cash loans	F	False	True	0	135000.0	312682.5	29686.5	297000.0	...	0.0	1.0	0.0	0.0	0.0	0.0	8.0	0.0	NaN	0.000000
4	0	Cash loans	M	False	True	0	121500.0	513000.0	21865.5	513000.0	...	0.0	0.0	0.0	0.0	0.0	0.0	6.0	0.0	1.0	0.600000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
307506	0	Cash loans	M	False	False	0	157500.0	254700.0	27558.0	225000.0	...	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.000000
307507	0	Cash loans	F	False	True	0	72000.0	269550.0	12001.5	225000.0	...	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	1.000000
307508	0	Cash loans	F	False	True	0	153000.0	677664.0	29979.0	585000.0	...	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	0.500000
307509	1	Cash loans	F	False	True	0	171000.0	370107.0	20205.0	319500.0	...	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	1.0	0.500000
307510	0	Cash loans	F	False	False	0	157500.0	675000.0	49117.5	675000.0	...	2.0	0.0	0.0	0.0	0.0	0.0	6.0	0.0	NaN	0.333333

307511 rows × 227 columns

Full DS size: 307511

	Total NaN Values	Proportion NaN (%)
ExtSource1	173378	56.0
ExtSource2	660	0.0
ExtSource3	60965	20.0

<module 'Draft.feature_builder_v2' from 'V:\\projects\\ppuodz-ML.4.1\\Draft\\feature_builder_v2.py'>

Full DS size: 307511



 -- 
 TEST SIZE: 30752 




Training: LGBMTuneAUC
sizes, train=276759, valid=6150, test=24602
Baseline_Only_CreditRatings: 2.0 seconds
Full DS size: 307511



 -- 
 TEST SIZE: 30752 




Training: LGBMTuneAUC
sizes, train=276759, valid=6150, test=24602
LGBM_AUC_Base_Features: 15.9 seconds
drop drop_cols_post_proc: 121
after drop_cols_post_proc: 59
Full DS size: 307511



 -- 
 TEST SIZE: 30752 




Training: LGBMTuneWeightedLogLossF1
sizes, train=276759, valid=6150, test=24602
LGBM_Weighted_LogLoss: 12.8 seconds

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\entityset\entityset.py:1914: UserWarning: index SK_BUREAU_ID not found in dataframe, creating new integer column
  warnings.warn(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\computational_backends\feature_set_calculator.py:828: FutureWarning: The provided callable <function max at 0x000001E47F474220> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
  ).agg(to_agg)
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\computational_backends\feature_set_calculator.py:828: FutureWarning: The provided callable <function min at 0x000001E47F474360> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
  ).agg(to_agg)
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\computational_backends\feature_set_calculator.py:828: FutureWarning: The provided callable <function mean at 0x000001E47F474C20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
  ).agg(to_agg)

Appending previous history
drop drop_cols_post_proc: 227
after drop_cols_post_proc: 121
Full DS size: 307511



 -- 
 TEST SIZE: 30752 




Training: LGBMTuneAUC
sizes, train=276759, valid=6150, test=24602
LGBM_AUC: 52.4 seconds

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\entityset\entityset.py:1914: UserWarning: index SK_BUREAU_ID not found in dataframe, creating new integer column
  warnings.warn(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\computational_backends\feature_set_calculator.py:828: FutureWarning: The provided callable <function max at 0x000001E47F474220> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
  ).agg(to_agg)
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\computational_backends\feature_set_calculator.py:828: FutureWarning: The provided callable <function min at 0x000001E47F474360> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
  ).agg(to_agg)
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\computational_backends\feature_set_calculator.py:828: FutureWarning: The provided callable <function mean at 0x000001E47F474C20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
  ).agg(to_agg)

Appending previous history
drop drop_cols_post_proc: 227
after drop_cols_post_proc: 121
Full DS size: 307511



 -- 
 TEST SIZE: 30752 

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\lightgbm\callback.py:325: UserWarning: Early stopping is not available in dart mode
  _log_warning('Early stopping is not available in dart mode')

Training: LGBMDartTuneAUC
sizes, train=276759, valid=6150, test=24602
LGBM_Dart_AUC_NEW: 97.6 seconds

C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\entityset\entityset.py:1914: UserWarning: index SK_BUREAU_ID not found in dataframe, creating new integer column
  warnings.warn(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\computational_backends\feature_set_calculator.py:828: FutureWarning: The provided callable <function max at 0x000001E47F474220> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
  ).agg(to_agg)
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\computational_backends\feature_set_calculator.py:828: FutureWarning: The provided callable <function min at 0x000001E47F474360> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
  ).agg(to_agg)
C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\computational_backends\feature_set_calculator.py:828: FutureWarning: The provided callable <function mean at 0x000001E47F474C20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
  ).agg(to_agg)

Appending previous history
Full DS size: 307511



 -- 
 TEST SIZE: 30752 




Training: LGBMTuneAUC
sizes, train=276759, valid=6150, test=24602
LGBM_AUC_All_Features: 47.2 seconds

Baseline_Only_CreditRatings:
  {'model__n_estimators': 300, 'model__learning_rate': 0.25, 'model__max_depth': 3, 'model__num_leaves': 224, 'model__min_gain_to_split': 1.5, 'model__min_data_in_leaf': 100, 'model__lambda_l1': 0, 'model__lambda_l2': 50, 'model__bagging_fraction': 1.0, 'model__feature_fraction': 0.30000000000000004, 'model__max_bin': 125}
LGBM_AUC_Base_Features:
  {'model__n_estimators': 800, 'model__learning_rate': 0.060000000000000005, 'model__max_depth': 7, 'model__num_leaves': 48, 'model__min_gain_to_split': 2.5, 'model__min_data_in_leaf': 300, 'model__lambda_l1': 80, 'model__lambda_l2': 5, 'model__bagging_fraction': 0.8, 'model__feature_fraction': 0.8, 'model__max_bin': 350}
LGBM_Weighted_LogLoss:
  {'model__n_estimators': 800, 'model__learning_rate': 0.06999999999999999, 'model__max_depth': 10, 'model__num_leaves': 224, 'model__min_gain_to_split': 3.5, 'model__min_data_in_leaf': 100, 'model__lambda_l1': 5, 'model__lambda_l2': 25, 'model__bagging_fraction': 0.4, 'model__feature_fraction': 0.6000000000000001, 'model__max_bin': 50}
LGBM_AUC:
  {'model__n_estimators': 700, 'model__learning_rate': 0.04, 'model__max_depth': 9, 'model__num_leaves': 120, 'model__min_gain_to_split': 0.5, 'model__min_data_in_leaf': 400, 'model__lambda_l1': 105, 'model__lambda_l2': 5, 'model__bagging_fraction': 0.30000000000000004, 'model__feature_fraction': 0.30000000000000004, 'model__max_bin': 500}
LGBM_Dart_AUC:
  {'model__class_weight': 'balanced', 'model__objective': 'binary', 'model__boosting_type': 'dart', 'model__n_estimators': 800, 'model__learning_rate': 0.19, 'model__max_depth': 10, 'model__num_leaves': 112, 'model__min_gain_to_split': 3.0, 'model__min_data_in_leaf': 2400, 'model__lambda_l1': 55, 'model__lambda_l2': 10, 'model__bagging_fraction': 0.2, 'model__feature_fraction': 0.6000000000000001, 'model__max_bin': 250, 'model__drop_rate': 0.025}
LGBM_AUC_All_Features:
  {'model__n_estimators': 700, 'model__learning_rate': 0.05, 'model__max_depth': 7, 'model__num_leaves': 224, 'model__min_gain_to_split': 4.0, 'model__min_data_in_leaf': 300, 'model__lambda_l1': 40, 'model__lambda_l2': 10, 'model__bagging_fraction': 0.7, 'model__feature_fraction': 0.8, 'model__max_bin': 100}

Model Results Comparison (5-fold CV)

	Model	auc	pr_auc	_f1_micro	_f1_macro	logloss	accuracy	precision_macro	recall_macro	f1_macro	target_f1	target_recall	target_precision	fbeta_1.5	fbeta_2.5	fbeta_4.0	log_loss	elapsed_time	total_size
4	LGBM_Dart_AUC	0.775	0.263	0.743	0.567	9.279	0.743	0.574	0.702	0.567	0.291	0.654	0.187	0.370	0.487	0.571	0.524	97.6	25.172626
3	LGBM_AUC	0.774	0.261	0.739	0.565	9.403	0.739	0.574	0.703	0.565	0.290	0.661	0.186	0.370	0.489	0.574	0.529	52.4	23.726562
5	LGBM_AUC_All_Features	0.773	0.262	0.740	0.566	9.361	0.740	0.574	0.703	0.566	0.290	0.658	0.186	0.370	0.488	0.573	0.526	47.2	42.107240
1	LGBM_AUC_Base_Features	0.759	0.243	0.715	0.548	10.267	0.715	0.566	0.691	0.548	0.273	0.661	0.172	0.352	0.475	0.566	0.561	15.9	22.553938
2	LGBM_Weighted_LogLoss	0.755	0.239	0.746	0.563	9.171	0.746	0.569	0.684	0.563	0.280	0.611	0.181	0.353	0.461	0.537	0.524	12.8	12.613545
0	Baseline_Only_CreditRatings	0.723	0.202	0.672	0.517	11.818	0.672	0.554	0.665	0.517	0.244	0.657	0.150	0.322	0.448	0.548	0.614	2.0	2.288661

Model Results Comparison (Test Set)

	Model	auc	pr_auc	_f1_micro	_f1_macro	logloss	accuracy	precision_macro	recall_macro	f1_macro	target_f1	target_recall	target_precision	fbeta_1.5	fbeta_2.5	fbeta_4.0	log_loss	elapsed_time	total_size
4	LGBM_Dart_AUC	0.781	0.273	0.744	0.568	9.237	0.744	0.575	0.710	0.568	0.293	0.670	0.187	0.374	0.495	0.582	0.520	97.6	25.172626
5	LGBM_AUC_All_Features	0.780	0.269	0.738	0.564	9.428	0.738	0.574	0.707	0.564	0.288	0.669	0.184	0.369	0.491	0.579	0.528	47.2	42.107240
3	LGBM_AUC	0.776	0.260	0.726	0.557	9.885	0.726	0.571	0.707	0.557	0.283	0.685	0.179	0.366	0.492	0.587	0.548	52.4	23.726562
1	LGBM_AUC_Base_Features	0.763	0.242	0.716	0.547	10.250	0.716	0.566	0.693	0.547	0.270	0.666	0.170	0.351	0.475	0.568	0.560	15.9	22.553938
2	LGBM_Weighted_LogLoss	0.759	0.239	0.736	0.557	9.510	0.736	0.568	0.689	0.557	0.275	0.632	0.176	0.352	0.466	0.549	0.537	12.8	12.613545
0	Baseline_Only_CreditRatings	0.730	0.204	0.673	0.519	11.773	0.673	0.555	0.672	0.519	0.246	0.671	0.150	0.325	0.454	0.558	0.613	2.0	2.288661

V:\projects\ppuodz-ML.4.1\shared\graph.py:1057: UserWarning: The figure layout has changed to tight
  plt.tight_layout()

Full DS size: 307511

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[10], line 11
      8 features_matrix = target_model_config[0].model_pipeline_config.load_data(
      9     loader_function=feature_builder_v2.load_datasets_and_prepare_features)
     10 # 
---> 11 features_all, labels_all = pipeline._get_features_labels(features_matrix)
     12 X_train, X_test, y_train, y_test = pipeline.get_deterministic_train_test_split(
     13     features_all, labels_all
     14 )
     16 X_train = X_train.drop(columns=["TARGET"])

NameError: name 'pipeline' is not defined

The calibration curve, also known as a reliability diagram, is a graphical representation used to evaluate the accuracy of predicted probabilities in classification models. It specifically checks how well the predicted probabilities of a model match the actual outcomes.

The ideal calibration curve is a straight line at a 45-degree angle from the bottom left to the top right of the plot. This line, often called the "line of perfect calibration," indicates that the model's predictions are perfectly calibrated. If your model predicts a class with 70% probability, then 70% of the cases that are predicted as such should indeed belong to that class.

Common Patterns and Their Interpretations: Perfect Calibration:

The points lie on the diagonal line from (0,0) to (1,1). Example: If a model predicts an event with 30% probability, then in the long run, that event occurs about 30% of the time when predicted at this probability. Underconfidence:

The curve lies above the diagonal line. The model's probabilities are lower than the true likelihood of the event. For instance, if events the model predicts to happen 60% of the time actually happen 80% of the time, the model is underconfident. Overconfidence:

The curve lies below the diagonal line. The model predicts higher probabilities than what is true. If a prediction of 80% only happens 60% of the time, the model is overconfident.

Expected Calibration Error (ECE): This measures the average difference between the predicted probabilities and the actual outcomes. Lower values indicate better calibration.

Brier Score: Measures the mean squared difference between the predicted probability and the actual outcome. It is a good measure of the accuracy and calibration of the predictions.

Residual Plots show the difference between observed and predicted probabilities. Helps in checking the assumption of homoscedasticity. Ideally, residuals should be randomly dispersed around the central line, and patterns suggest model inadequacies.

homoscedasticity

Based on the chart we see these potential problems: Probability Estimates are Polarized:

The cup-like pattern at the top suggests the model is very confident (probabilities close to 0 or 1) about certain instances but is incorrect, as these points have higher residuals. The bottom lines being straighter and closer to zero indicate that for a range of predicted probabilities, the residuals are consistently low, which means the model performs well in that range.

Model Overconfidence:

The residuals are larger for predictions near 0 or 1 because the log loss penalizes confident incorrect predictions more harshly than less confident ones. This overconfidence is often characteristic of models that are not well-calibrated and could benefit from probability calibration techniques.

Class Imbalance:

This pattern can sometimes emerge from class imbalance if the model is better at predicting the majority class and frequently mispredicts the minority class with high confidence.

Non-linearity in Feature Space:

The curving pattern could also be a sign that the model is not capturing some non-linear relationships between features and the outcome. This might suggest that feature engineering or a more sophisticated model could be helpful.

Mean Absolute Error (MAE) of Residuals: This is the average of the absolute values of the residuals. It gives an idea of the average magnitude of the prediction errors. Annotation: Indicating MAE on the residual plot can help assess the typical error magnitude in a more intuitive way than just viewing the spread of residuals.

Maximum Residual: The maximum value among the residuals can indicate the worst-case scenario for your predictions. Annotation: Marking the maximum residual can alert users to the worst errors the model could make.

In the finance industry, loan grades (or credit scores) are a crucial part of risk management, helping lenders assess the creditworthiness of borrowers. These grades are typically determined based on various factors, including the borrower's credit history, income stability, debt-to-income ratio, and more. The grades reflect the estimated risk of default, and they directly influence the interest rate offered to the borrower. Commonly, loan grades are categorized from 'A' (lowest risk) to 'G' (highest risk), although the specific categories can vary by institution.

A (Lowest Risk): Below 1% default rate. Borrowers with excellent credit histories and very low risk of default. B: 1% to 3% default rate. Borrowers with good credit histories and low risk of default. C: 3% to 7% default rate. Borrowers with average credit histories and moderate risk of default. D: 7% to 15% default rate. Borrowers with below-average credit histories and higher risk of default. E: 15% to 25% default rate. Borrowers with poor credit histories and very high risk of default. F and G (Highest Risk): Above 25% default rate. Borrowers with very poor credit histories and extremely high risk of default.

Full DS size: 307511

	Total NaN Values	Proportion NaN (%)
PrevRatioRejectedAccepted	16847	5.0

Exploratory Analysis

This notebooks includes the analysis of selected variables (based on their importance at predicting the target variable) and their relationships. Individual analysis of each variable is available in the EDA_appendices notebook.

Dataset Summary

NaN Values by Column:

	Total NaN Values	Proportion NaN (%)
ExtSource2	660	0.0
ExtSource3	60965	20.0
ExtSource1	173378	56.0
AmtGoodsPrice	278	0.0
OwnCarAge	202929	66.0
PrevAmtDownPaymentSum	16454	5.0
AmtAnnuity	12	0.0
MeanbureaudaysCredit	44020	14.0
MeanbureauamtCreditSumDebt	51380	17.0
PrevAvgYieldGroup	18945	6.0
PrevCreditReceivedRequestedDiff	16454	5.0
OccupationType	96391	31.0
PrevRatioRejectedAccepted	16847	5.0
MaxbureaudaysCreditEnddate	46269	15.0
PrevLastLoanGoodsCategory	16454	5.0
MeanbureauamtCreditMaxOverdue	123625	40.0

Duplicates Values

'Duplicated Values: 0'

'Total Columns: 229'

Correlations

Because we has such a large number of columns we have only included features whhich have an importance value { > X } with our final LGBM model: TODO

V:\projects\ppuodz-ML.4.1\shared\graph.py:1276: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
  corr = round(corr.applymap(pd.to_numeric), 2)

The TARGET variable (loans with payment difficulties) is most correlated with credit ratings obtained from external sources. The correlation is very weak but still significant.

`` Because the datatypes of features vary we had to use different methods to measure the strength and significance of each pair:

• Chi-Squared Test: Assesses independence between two categorical variables. For bool-bool pairs due to categorical nature.

• Point Biserial Correlation: Measures correlation between a binary and a continuous variable. For bool-numerical pairs to account for mixed data types.

• Spearman's Rank Correlation: Assesses monotonic relationship between two continuous variables. Used for numerical-numerical pairs (for non-normally distributed data).

Since the Chi-Squared test outputs an unbound statistic/value which can't be directly compared to pointbiserialr or Spearman Rank we have converted them to a Cramér's V: value which is normalized between 0 and 1. This was done to make the values in the matrix more uniform however we must note that Cramér's V and Spearman's correlation coefficients are fundamentally different statistics and generally can't be directly compared.

Corelation With the Target Variable

Our target variable TARGET show whether the given application had any late payments (value = 1), we can see that no single feature is strongly correlated with it:

	Coefficient	P-Value
Column
ExtSource3	-0.161	0.000
ExtSource1	-0.131	0.000
ExtSource2	-0.128	0.000
MeanbureaudaysCredit	0.093	0.000
OccupationType	0.075	0.000
DaysEmployed	0.074	0.000
PrevRatioRejectedAccepted	0.073	0.000
PrevRatioRejectedAccepted_cats_2	0.072	0.000
PrevRatioRejectedAccepted_cats	0.072	0.000
OrganizationType	0.069	0.000
NameEducationType	0.067	0.000
PrevAmtDownPaymentSum	-0.057	0.000
PrevCreditReceivedRequestedDiff	0.055	0.000
DaysBirth	0.053	0.000
PrevLastLoanGoodsCategory	0.051	0.000
OwnCarAge	0.050	0.000
MeanbureauamtCreditSumDebt	0.049	0.000
MeanbureauamtCreditMaxOverdue	0.044	0.000
DaysIdPublish	0.042	0.000
CodeGender	0.041	0.000
PrevAvgYieldGroup	0.040	0.000
FlagDocument3	0.039	0.000
AmtGoodsPrice	-0.034	0.000
MaxbureaudaysCreditEnddate	0.034	0.000
NameFamilyStatus	0.027	0.002
AmtCredit	-0.023	0.001
AmtAnnuity	0.003	0.664

The chart below shows the relationship between selected categorical variables and loan status. E.g. a significantly higher proportion of loans taken out by males had issues.

CategoricalDtype(categories=['< 25% Rejected', '> 25% Rejected', 'All Accepted', 'No Previous App.'], ordered=False, categories_dtype=object)

Relationships Between Numerical and Categorical Variables

The charts below show pairs of numerical and categorical features (including some binned numerical features) that have a signficant relationships and at least a small effect size (eta_squared>0.01) based on the non-parametric Kruskal-Wallis Test (one-way ANOVA on ranks) testing whether samples originate from the same distribution.

*It's similar to the Mann–Whitney U test but allows comparing more than 2 groups

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  group_counts = _df.groupby(c).size()

Analyzing Credit Scores (ExtSource1)

ExtSource1/2/3 are the variables most strongly correlated with the target variable, they indicate client credit scores obtained from external sources. While th correlation coeficients are very low (only slightly above 0.1) we'll look a bit more into these scores because ussually credit ratings tend be the most useful metric when estimating the risk of specific loans:

C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\2151574185.py:16: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
  colors = plt.cm.get_cmap('tab10', 4)

Summary for combined model:
                           Logit Regression Results                           
==============================================================================
Dep. Variable:                 TARGET   No. Observations:               109589
Model:                          Logit   Df Residuals:                   109585
Method:                           MLE   Df Model:                            3
Date:                Mon, 29 Apr 2024   Pseudo R-squ.:                  0.1047
Time:                        19:57:19   Log-Likelihood:                -25636.
converged:                       True   LL-Null:                       -28634.
Covariance Type:            nonrobust   LLR p-value:                     0.000
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
const          0.6002      0.040     14.829      0.000       0.521       0.680
ExtSource1    -2.0989      0.061    -34.382      0.000      -2.219      -1.979
ExtSource2    -1.9640      0.060    -32.654      0.000      -2.082      -1.846
ExtSource3    -2.7793      0.062    -44.483      0.000      -2.902      -2.657
==============================================================================

This is a simple Logistic model that just uses the credit scores to estimate the target variable. The confidence interval shows the the standard deviation of the residuals from a combined logistic regression model (residuals in this context are the differences between the observed values (y_combined) and the predicted probabilities).

Gennerally the explained variabity (Pseudo R-squared) is very quite low at only 0.1047 however the model itself is statistically significant (LLR p-value = 0.0)

	Coefficient	Standard Error	P-Value	Conf. Interval Lower	Conf. Interval Upper
const	0.600	0.040	0.0	0.521	0.680
ExtSource1	-2.099	0.061	0.0	-2.219	-1.979
ExtSource2	-1.964	0.060	0.0	-2.082	-1.846
ExtSource3	-2.779	0.062	0.0	-2.902	-2.657

Normalized credit ratings from three sources are inversely related to default risk, with ExtSource3 having the strongest influence. We can see that a basic Logistic model can already provide a reasonably high result (AUC = 0.74). However, we have to note that the results are based on the full training set and are only provided for EDA/feature analysis purposes. Full statistical modelling will be done in further sections.

C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\1233466688.py:6: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(data=features_matrix[features_matrix['TARGET'] == 1][col],
C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\1233466688.py:8: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(data=features_matrix[features_matrix['TARGET'] == 0][col], label=f'{col} - No Default', shade=True)

C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\1233466688.py:6: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(data=features_matrix[features_matrix['TARGET'] == 1][col],
C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\1233466688.py:8: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(data=features_matrix[features_matrix['TARGET'] == 0][col], label=f'{col} - No Default', shade=True)

C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\1233466688.py:6: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(data=features_matrix[features_matrix['TARGET'] == 1][col],
C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\1233466688.py:8: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(data=features_matrix[features_matrix['TARGET'] == 0][col], label=f'{col} - No Default', shade=True)

We can see that while the the external credit are clearly related to default risk their explanatory power is somewhat limited because there is still a large amount of overlap (especially for ExtSource2, however it's coeefficient in our logistical model is similar to that of ExtSource1.

Previous Application History

Did any clients had previously applied for loans with Home Credit and what were the outcomes of their applications?

PrevRatioRejectedAccepted_cats
All Accepted        190370
> 25% Rejected       66215
< 25% Rejected       34079
No Previous App.     16847
Name: count, dtype: int64

Did any applicants default on any previous loans?

TotalDefaults_cats
No Defaults          304114
1 Defaulted Loans      3397
Name: count, dtype: int64

Suprisingly we can see that a ~1% of all applicants who were granted a loans have previously had payment difficulties with a previous loans at Home Credit. This is quite interesting considering that gennerally credit instituions are reluctant to offer loans again to problematic clients.

Total "Defaults"/Loans With Payment Difficulties per applicant:

	TotalDefaults	count	proportion
0	0.0	304114	0.99
1	1.0	3177	0.01
2	2.0	163	0.00
3	3.0	38	0.00
4	4.0	11	0.00
5	5.0	4	0.00
6	6.0	3	0.00
7	7.0	1	0.00

Previous Loan History and Default Risk

The chart below shows the default rate based on whether applicant has previous applied for loans with Home Cred: No Previous App. - no previous applications for client found (i.e. new clients) All Accepted - all previous applications were accepted < 25% Rejected - less than 1/4 applications were rejected > 25% Rejected - more than 1/4 applications were rejected

Interestingly we can see that while applicants whose previous loans were rejected are significantly more likely to default when finally given a loan previous clients with no failed applications have a higher default risk than new clients.

This likely limits the usefulness of the previous_application table because only a small proportion of clients have previously rejected applications

<Figure size 1000x600 with 0 Axes>

C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\4178975199.py:5: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(data=features_matrix[features_matrix['AnyPreviousRejections'] == 1][col],
C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\4178975199.py:7: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(data=features_matrix[features_matrix['AnyPreviousRejections'] == 0][col], label=f'{col} - No Rejections', shade=True)

C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\3528939979.py:10: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(data=features_matrix[features_matrix['AnyPreviousDefaults'] == 1][col],
C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\3528939979.py:12: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(data=features_matrix[features_matrix['AnyPreviousDefaults'] == 0][col], label=f'{col} - No Rejections', shade=True)

<Figure size 1200x600 with 0 Axes>

C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\3528939979.py:10: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(data=features_matrix[features_matrix['AnyPreviousDefaults'] == 1][col],
C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\3528939979.py:12: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(data=features_matrix[features_matrix['AnyPreviousDefaults'] == 0][col], label=f'{col} - No Rejections', shade=True)

C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\3528939979.py:10: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(data=features_matrix[features_matrix['AnyPreviousDefaults'] == 1][col],
C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\3528939979.py:12: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(data=features_matrix[features_matrix['AnyPreviousDefaults'] == 0][col], label=f'{col} - No Rejections', shade=True)

We can clearly see that clients who had run into payment issues with their past loans tend to have a signficantly lower credit ExtSource3 however there is almost no difference with other scores. This incidates that the data from Home Credit itself is only included in the third rating (which might explain its higher explantatory power in our Logistic model)

AmtIncomeTotal
AmtCredit
AmtAnnuity
AmtGoodsPrice
AmtReqCreditBureauHour
AmtReqCreditBureauDay
AmtReqCreditBureauWeek
AmtReqCreditBureauMon
AmtReqCreditBureauQrt
AmtReqCreditBureauYear
MaxbureauamtAnnuity
MaxbureauamtCreditMaxOverdue
MaxbureauamtCreditSum
MaxbureauamtCreditSumDebt
MaxbureauamtCreditSumLimit
MaxbureauamtCreditSumOverdue
MeanbureauamtAnnuity
MeanbureauamtCreditMaxOverdue
MeanbureauamtCreditSum
MeanbureauamtCreditSumDebt
MeanbureauamtCreditSumLimit
MeanbureauamtCreditSumOverdue
MinbureauamtAnnuity
MinbureauamtCreditMaxOverdue
MinbureauamtCreditSum
MinbureauamtCreditSumDebt
MinbureauamtCreditSumLimit
MinbureauamtCreditSumOverdue
PrevAmtApplicationMean
PrevAmtApplicationSum
PrevAmtCreditMean
PrevAmtCreditSum
PrevAmtDownPaymentSum

Loan Purposes

V:\projects\ppuodz-ML.4.1\shared\graph.py:1529: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  

C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\4000396085.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  prop_df = features_matrix.groupby('NameContractType')['TARGET'].value_counts(normalize=True).unstack().fillna(0)

<Figure size 1000x600 with 0 Axes>

EDA Summary

The EDA was performed in paralel with performing feature enginerring (aggregation of non-main tables) and building an initial LGBM model (using all features), to minimize unnecessary complexity only features which have some importance { > X } are included.

index SK_BUREAU_ID not found in dataframe, creating new integer column

Appending previous history

Missing Values

'AMT_CREDIT'

	TARGET	NameContractType	CodeGender	FlagOwnCar	FlagOwnRealty	CntChildren	AmtIncomeTotal	AmtCredit	AmtAnnuity	AmtGoodsPrice	NameTypeSuite	NameIncomeType	NameEducationType	NameFamilyStatus	NameHousingType	RegionPopulationRelative	DaysBirth	DaysEmployed	DaysRegistration	DaysIdPublish	OwnCarAge	FlagMobil	FlagEmpPhone	FlagWorkPhone	FlagContMobile	FlagPhone	FlagEmail	OccupationType	CntFamMembers	RegionRatingClient	RegionRatingClientWCity	WeekdayApprProcessStart	HourApprProcessStart	RegRegionNotLiveRegion	RegRegionNotWorkRegion	LiveRegionNotWorkRegion	RegCityNotLiveCity	RegCityNotWorkCity	LiveCityNotWorkCity	OrganizationType	ExtSource1	ExtSource2	ExtSource3	ApartmentsAvg	BasementareaAvg	YearsBeginexpluatationAvg	YearsBuildAvg	CommonareaAvg	ElevatorsAvg	EntrancesAvg	FloorsmaxAvg	FloorsminAvg	LandareaAvg	LivingapartmentsAvg	LivingareaAvg	NonlivingapartmentsAvg	NonlivingareaAvg	ApartmentsMode	BasementareaMode	YearsBeginexpluatationMode	YearsBuildMode	CommonareaMode	ElevatorsMode	EntrancesMode	FloorsmaxMode	FloorsminMode	LandareaMode	LivingapartmentsMode	LivingareaMode	NonlivingapartmentsMode	NonlivingareaMode	ApartmentsMedi	BasementareaMedi	YearsBeginexpluatationMedi	YearsBuildMedi	CommonareaMedi	ElevatorsMedi	EntrancesMedi	FloorsmaxMedi	FloorsminMedi	LandareaMedi	LivingapartmentsMedi	LivingareaMedi	NonlivingapartmentsMedi	NonlivingareaMedi	FondkapremontMode	HousetypeMode	TotalareaMode	WallsmaterialMode	EmergencystateMode	Obs30CntSocialCircle	Def30CntSocialCircle	Obs60CntSocialCircle	Def60CntSocialCircle	DaysLastPhoneChange	FlagDocument2	FlagDocument3	FlagDocument4	FlagDocument5	FlagDocument6	...	MinbureauamtCreditSum	MinbureauamtCreditSumDebt	MinbureauamtCreditSumLimit	MinbureauamtCreditSumOverdue	MinbureaucntCreditProlong	MinbureaucreditDayOverdue	MinbureaudaysCredit	MinbureaudaysCreditEnddate	MinbureaudaysCreditUpdate	MinbureaudaysEnddateFact	MinbureauskIdBureau	ActiveLoansCount	TotalDefaults	DefaultRatio	LastLoanIssuedDays	PrevAmtApplicationMean	PrevAmtApplicationSum	PrevAmtCreditMean	PrevAmtCreditSum	PrevAmtDownPaymentSum	PrevRateInterestPrimaryMean	PrevRateInterestPrimaryStd	PrevTotalDpdSum	PrevHasAnyDpdMean	PrevHasAnyDpdSum	PrevMonthsWithDpdPropMean	PrevMonthsWithDpdPropSum	PrevTotalPreviousLoans	PrevCreditReceivedRequestedDiff	PrevRatioSumDownPaymentCredit	PrevLastLoanInterestRate	PrevLastLoanPurpose	PrevLastLoanContractStatus	PrevLastLoanDecisionDate	PrevLastLoanPaymentType	PrevLastLoanCodeRejectReason	PrevLastLoanClientType	PrevLastLoanPortfolio	PrevLastLoanGoodsCategory	PrevLastLoanProductType	PrevLastLoanYieldGroup	PrevContractStatusApprovedCount	PrevContractStatusCanceledCount	PrevContractStatusRefusedCount	PrevContractStatusUnusedofferCount	PrevPortfolioCardsCount	PrevPortfolioCarsCount	PrevPortfolioCashCount	PrevPortfolioPosCount	PrevPortfolioXnaCount	PrevProductTypeXnaCount	PrevProductTypeWalkinCount	PrevProductTypeXsellCount	PrevAvgYieldGroup	PrevDaysAfterFirstApplication	PrevCurrentlyActiveLoans	PrevApprovedLoans	PrevCanceledLoans	PrevRefusedLoans	PrevUnusedofferLoans	PrevTotalLoans	PrevAcceptedToTotalRatio	PrevCancelledToTotalRatio	PrevRefusedToTotalRatio	PrevUnusedToTotalRatio	PrevRatioRejectedAccepted	PrevCodeRejectReasonClientCount	PrevCodeRejectReasonHcCount	PrevCodeRejectReasonLimitCount	PrevCodeRejectReasonScoCount	PrevCodeRejectReasonScofrCount	PrevCodeRejectReasonSystemCount	PrevCodeRejectReasonVerifCount	PrevCodeRejectReasonXapCount	PrevCodeRejectReasonXnaCount	PrevLastLoanNflagInsuredOnApproval	PrevAvgNflagInsuredOnApproval	ExtSource2_binned	ExtSource3_binned	ExtSource1_binned	DaysEmployed_binned	AmtCredit_binned	OwnCarAge_binned	PrevCreditReceivedRequestedDiff_binned	DaysBirth_binned	PrevAmtDownPaymentSum_binned	MeanbureauamtCreditSumDebt_binned	MaxbureaudaysCreditEnddate_binned	PrevAvgYieldGroup_binned	MeanbureauamtCreditMaxOverdue_binned	AmtGoodsPrice_binned	DaysLastPhoneChange_binned	PrevRatioRejectedAccepted_binned	DaysIdPublish_binned	AmtAnnuity_binned	AmtIncomeTotal_binned	FlagDocument3_binned	MaxbureaudaysCredit_binned	PrevAcceptedToTotalRatio_binned	MaxbureaudaysEnddateFact_binned
0	0	Cash loans	M	True	False	2	207000.0	465457.5	52641.0	418500.0	Unaccompanied	Commercial associate	Secondary / secondary special	Married	House / apartment	0.009630	-13297	-762	-637.0	-4307	19	1	1	0	1	0	0	Sales staff	4	2	2	THURSDAY	11	0	0	0	0	1	1	Business Entity Type 3	0.675878	0.604894	0.000527	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	<NA>	0	0	0	0	-2.0	0	1	0	0	0	...	19449.0	0.00	0.0	0.0	0.0	0.0	-2857.0	-2710.0	-1033.0	-1036.0	5022129.0	3.0	0.0	0.0	-225	22279.500000	111397.50	100030.500000	500152.50	6642.0	NaN	NaN	0.0	0.0	0.0	0.0	0.0	8.0	388755.0	0.013280	NaN	XNA	Refused	-1291.0	Cash through the bank	HC	Repeater	Cash	XNA	walk-in	high	2.0	0.0	3.0	0.0	2.0	0.0	1.0	2.0	0.0	2.0	3.0	0.0	1.666667	-546.0	NaN	2.0	0.0	3.0	0.0	5.0	0.400000	0.000000	0.600000	0.000000	1.000000	0.0	3.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	NaN	0.000000	0.57 - 0.66	0.0 - 0.37	0.68 - 0.96	-1211.0 - -289.0	270000.0 - 512338.5	15 - 91	117146.25 - 10985769.0	-15739.0 - -12412.0	6111.0 - 17820.0	0.0 - 43926.19	120.0 - 906.0	1.33 - 2.0	0.0 - 2076.99	238500.0 - 450000.0	-279.0 - 0.0	0.25 - 13.6	-6551.0 - -4305.0	34587.0 - 230161.5	202500.0 - 117000000.0	1	-143.0 - -1.0	0.0 - 0.5	-347.0 - -153.0
1	0	Cash loans	F	True	True	0	247500.0	1281712.5	48946.5	1179000.0	Unaccompanied	Commercial associate	Higher education	Single / not married	House / apartment	0.006852	-14778	-1141	-1610.0	-4546	11	1	1	0	1	0	1	Managers	1	3	3	THURSDAY	10	0	0	0	0	0	0	Business Entity Type 3	0.430827	0.425351	0.712155	0.0753	0.0568	0.9970	0.9592	0.1326	0.08	0.0517	0.4167	0.2917	0.0735	0.0601	0.0844	0.0058	0.1118	0.0756	0.0566	0.9940	0.9216	0.0523	0.0806	0.0345	0.3333	0.0417	0.0445	0.0652	0.0857	0.0	0.0000	0.0760	0.0568	0.9970	0.9597	0.1335	0.08	0.0517	0.4167	0.2917	0.0748	0.0611	0.0859	0.0058	0.1142	reg oper account	block of flats	0.0754	Monolithic	False	2	0	2	0	-1071.0	0	1	0	0	0	...	190867.5	0.00	0.0	0.0	0.0	0.0	-1487.0	-391.0	-765.0	-765.0	5977750.0	2.0	0.0	0.0	-52	454259.250000	5451111.00	488020.875000	5856250.50	36603.0	NaN	NaN	0.0	0.0	0.0	0.0	0.0	8.0	405139.5	0.006250	NaN	XAP	Refused	-757.0	XNA	HC	Repeater	Cards	XNA	walk-in	XNA	10.0	0.0	2.0	0.0	2.0	0.0	7.0	3.0	0.0	3.0	3.0	6.0	1.000000	-245.0	NaN	10.0	0.0	2.0	0.0	12.0	0.833333	0.000000	0.166667	0.000000	0.181818	0.0	1.0	1.0	0.0	0.0	0.0	0.0	10.0	0.0	NaN	0.555556	0.39 - 0.57	0.67 - 0.89	0.33 - 0.51	-1211.0 - -289.0	808650.0 - 4050000.0	9 - 15	117146.25 - 10985769.0	-15739.0 - -12412.0	17820.0 - 3960000.0	-220213.42 - 0.0	1682.0 - 31199.0	0.5 - 1.0	2076.99 - 47406123.0	679500.0 - 4050000.0	-1571.0 - -762.0	0.0 - 0.25	-6551.0 - -4305.0	34587.0 - 230161.5	202500.0 - 117000000.0	1	-300.0 - -143.0	0.8 - 1.0	-153.0 - 0.0
2	0	Cash loans	F	True	False	0	202500.0	495000.0	39109.5	495000.0	Unaccompanied	Working	Secondary / secondary special	Married	House / apartment	0.035792	-17907	-639	-2507.0	-1461	4	1	1	1	1	0	0	Sales staff	2	2	2	TUESDAY	16	0	0	0	0	0	0	Self-employed	0.527239	0.531760	0.207964	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	<NA>	5	0	5	0	-1435.0	0	1	0	0	0	...	179550.0	0.00	0.0	0.0	0.0	0.0	-2305.0	-2123.0	-1706.0	-2123.0	5353341.0	3.0	0.0	0.0	-394	121017.818571	847124.73	126556.547143	885895.83	12330.9	NaN	NaN	0.0	0.0	0.0	0.0	0.0	13.0	38771.1	0.013919	NaN	XNA	Canceled	-107.0	XNA	XAP	Repeater	XNA	XNA	XNA	XNA	3.0	2.0	1.0	1.0	0.0	0.0	1.0	2.0	4.0	6.0	0.0	1.0	1.000000	-2835.0	NaN	3.0	2.0	1.0	1.0	7.0	0.428571	0.285714	0.142857	0.142857	0.250000	1.0	1.0	0.0	0.0	0.0	0.0	0.0	5.0	0.0	NaN	0.000000	0.39 - 0.57	0.0 - 0.37	0.51 - 0.68	-1211.0 - -289.0	270000.0 - 512338.5	0 - 5	16020.0 - 117146.25	-19681.0 - -15739.0	6111.0 - 17820.0	141482.43 - 43650000.0	906.0 - 1682.0	0.5 - 1.0	NaN	450000.0 - 679500.0	-1571.0 - -762.0	0.0 - 0.25	-1728.0 - 0.0	34587.0 - 230161.5	148500.0 - 202500.0	1	-624.0 - -300.0	0.0 - 0.5	-718.0 - -347.0
3	0	Cash loans	F	False	True	0	247500.0	254700.0	24939.0	225000.0	Unaccompanied	State servant	Secondary / secondary special	Widow	House / apartment	0.046220	-19626	-6982	-11167.0	-3158	<NA>	1	1	0	1	0	0	High skill tech staff	1	1	1	FRIDAY	14	0	0	0	0	0	0	Business Entity Type 3	NaN	0.693521	0.614414	0.1320	0.0645	0.9846	NaN	NaN	0.16	0.0690	0.6250	NaN	NaN	NaN	0.1628	NaN	0.0022	0.1345	0.0670	0.9846	NaN	NaN	0.1611	0.0690	0.6250	NaN	NaN	NaN	0.1696	NaN	0.0023	0.1332	0.0645	0.9846	NaN	NaN	0.16	0.0690	0.6250	NaN	NaN	NaN	0.1657	NaN	0.0022	NaN	NaN	0.1285	Panel	False	0	0	0	0	-2000.0	0	1	0	0	0	...	38268.0	0.00	0.0	0.0	0.0	0.0	-2657.0	-2322.0	-2321.0	-2321.0	5347375.0	0.0	0.0	0.0	-1234	104292.000000	104292.00	103153.500000	103153.50	10431.0	NaN	NaN	0.0	0.0	0.0	0.0	0.0	7.0	-1138.5	0.101121	NaN	XAP	Approved	-2000.0	Cash through the bank	XAP	New	POS	Audio/Video	XNA	high	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	2.000000	-2599.0	NaN	1.0	0.0	0.0	0.0	1.0	1.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.000000	0.66 - 0.85	0.54 - 0.67	NaN	-17912.0 - -2760.25	45000.0 - 270000.0	NaN	-3960000.0 - -558.0	-19681.0 - -15739.0	6111.0 - 17820.0	-220213.42 - 0.0	-41875.0 - 120.0	1.33 - 2.0	2076.99 - 47406123.0	45000.0 - 238500.0	-4131.0 - -1571.0	0.0 - 0.25	-3269.0 - -1728.0	24916.5 - 34587.0	202500.0 - 117000000.0	1	-2922.0 - -624.0	0.8 - 1.0	-2858.0 - -718.0
4	0	Cash loans	M	False	True	0	112500.0	308133.0	15862.5	234000.0	Unaccompanied	Working	Secondary / secondary special	Single / not married	House / apartment	0.018850	-20327	-1105	-7299.0	-494	<NA>	1	1	0	1	0	0	Laborers	1	2	2	WEDNESDAY	11	0	0	0	0	0	0	Business Entity Type 3	0.654882	0.560690	0.636376	0.0619	0.0553	0.9717	NaN	NaN	0.00	0.1724	0.1667	NaN	0.0866	NaN	0.0749	NaN	0.0149	0.0630	0.0574	0.9717	NaN	NaN	0.0000	0.1724	0.1667	NaN	0.0885	NaN	0.0780	NaN	0.0158	0.0625	0.0553	0.9717	NaN	NaN	0.00	0.1724	0.1667	NaN	0.0881	NaN	0.0762	NaN	0.0152	NaN	block of flats	0.0765	Stone, brick	False	0	0	0	0	-173.0	0	1	0	0	0	...	28575.0	0.00	0.0	0.0	0.0	0.0	-2618.0	-2430.0	-2415.0	-2430.0	6293239.0	1.0	0.0	0.0	-693	70180.000000	631620.00	177702.500000	1599322.50	2475.0	NaN	NaN	0.0	0.0	0.0	0.0	0.0	7.0	967702.5	0.001548	NaN	XNA	Canceled	-173.0	XNA	XAP	Repeater	XNA	XNA	XNA	XNA	5.0	1.0	3.0	0.0	2.0	0.0	4.0	2.0	1.0	3.0	1.0	5.0	1.666667	-2504.0	NaN	5.0	1.0	3.0	0.0	9.0	0.555556	0.111111	0.333333	0.000000	0.500000	0.0	2.0	0.0	1.0	0.0	0.0	0.0	6.0	0.0	NaN	0.600000	0.39 - 0.57	0.54 - 0.67	0.51 - 0.68	-1211.0 - -289.0	270000.0 - 512338.5	NaN	117146.25 - 10985769.0	-25201.0 - -19681.0	0.0 - 6111.0	141482.43 - 43650000.0	906.0 - 1682.0	1.33 - 2.0	NaN	45000.0 - 238500.0	-279.0 - 0.0	0.25 - 13.6	-1728.0 - 0.0	1980.0 - 16573.5	25650.0 - 112500.0	1	-300.0 - -143.0	0.5 - 0.8	-718.0 - -347.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
99995	0	Revolving loans	F	False	True	1	202500.0	585000.0	29250.0	585000.0	Unaccompanied	Working	Secondary / secondary special	Married	House / apartment	0.010147	-13827	-1317	-398.0	-1172	<NA>	1	1	0	1	0	0	Sales staff	3	2	2	MONDAY	9	0	0	0	0	0	0	Self-employed	0.678014	0.591704	0.456110	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	<NA>	0	0	0	0	-967.0	0	0	0	0	0	...	0.0	0.00	0.0	0.0	0.0	0.0	-1043.0	-766.0	-710.0	-766.0	6624105.0	5.0	0.0	0.0	-504	38610.000000	115830.00	38770.500000	116311.50	9220.5	NaN	NaN	0.0	0.0	0.0	0.0	0.0	7.0	481.5	0.079274	NaN	XNA	Approved	-371.0	XNA	XAP	Repeater	Cash	XNA	x-sell	middle	3.0	0.0	0.0	0.0	0.0	0.0	1.0	2.0	0.0	2.0	0.0	1.0	1.000000	-2887.0	NaN	3.0	0.0	0.0	0.0	3.0	1.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	1.0	0.333333	0.57 - 0.66	0.37 - 0.54	0.68 - 0.96	-2760.25 - -1211.0	512338.5 - 808650.0	NaN	-558.0 - 16020.0	-15739.0 - -12412.0	6111.0 - 17820.0	0.0 - 43926.19	906.0 - 1682.0	0.5 - 1.0	0.0 - 2076.99	450000.0 - 679500.0	-1571.0 - -762.0	0.0 - 0.25	-1728.0 - 0.0	24916.5 - 34587.0	148500.0 - 202500.0	0	-624.0 - -300.0	0.8 - 1.0	-718.0 - -347.0
99996	0	Cash loans	M	False	True	0	225000.0	562500.0	31540.5	562500.0	Unaccompanied	Working	Secondary / secondary special	Married	House / apartment	0.018209	-20956	-3053	-13427.0	-4280	<NA>	1	1	0	1	1	0	Drivers	2	3	3	FRIDAY	12	0	0	0	0	0	0	Business Entity Type 3	NaN	0.140261	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	<NA>	2	1	2	1	-842.0	0	1	0	0	0	...	3127500.0	NaN	NaN	0.0	0.0	0.0	-1914.0	994.0	-498.0	-498.0	5160093.0	0.0	0.0	0.0	-498	375000.000000	1125000.00	531804.000000	1595412.00	0.0	NaN	NaN	0.0	0.0	0.0	0.0	0.0	7.0	470412.0	0.000000	NaN	XAP	Approved	-542.0	XNA	XAP	Repeater	Cards	XNA	x-sell	XNA	3.0	0.0	0.0	0.0	1.0	0.0	2.0	0.0	0.0	0.0	1.0	2.0	1.500000	-2842.0	NaN	3.0	0.0	0.0	0.0	3.0	1.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	0.333333	0.0 - 0.39	NaN	NaN	-17912.0 - -2760.25	512338.5 - 808650.0	NaN	117146.25 - 10985769.0	-25201.0 - -19681.0	0.0 - 6111.0	NaN	906.0 - 1682.0	1.33 - 2.0	NaN	450000.0 - 679500.0	-1571.0 - -762.0	0.0 - 0.25	-4305.0 - -3269.0	24916.5 - 34587.0	202500.0 - 117000000.0	1	-2922.0 - -624.0	0.8 - 1.0	-718.0 - -347.0
99997	0	Revolving loans	M	True	False	1	135000.0	180000.0	9000.0	180000.0	Family	Working	Higher education	Married	House / apartment	0.035792	-10578	-592	-5307.0	-3257	11	1	1	0	1	0	0	Core staff	3	2	2	SATURDAY	14	0	0	0	0	0	0	Self-employed	0.602777	0.487365	0.490258	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	<NA>	0	0	0	0	-2521.0	0	0	0	0	0	...	91593.0	49016.43	0.0	0.0	0.0	0.0	-229.0	206.0	-3.0	NaN	5967472.0	2.0	0.0	0.0	0	103455.000000	103455.00	93109.500000	93109.50	10345.5	NaN	NaN	0.0	0.0	0.0	0.0	0.0	10.0	-10345.5	0.111111	NaN	XAP	Approved	-320.0	Cash through the bank	XAP	New	POS	Computers	XNA	middle	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	1.000000	-2702.0	2.0	1.0	0.0	0.0	0.0	1.0	1.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.000000	0.39 - 0.57	0.37 - 0.54	0.51 - 0.68	-1211.0 - -289.0	45000.0 - 270000.0	0 - 5	-3960000.0 - -558.0	-12412.0 - -7673.0	6111.0 - 17820.0	141482.43 - 43650000.0	906.0 - 1682.0	0.5 - 1.0	0.0 - 2076.99	45000.0 - 238500.0	-4131.0 - -1571.0	0.0 - 0.25	-3269.0 - -1728.0	1980.0 - 16573.5	112500.0 - 148500.0	0	-143.0 - -1.0	0.8 - 1.0	NaN
99998	0	Cash loans	F	True	True	0	135000.0	254700.0	17149.5	225000.0	Unaccompanied	Working	Incomplete higher	Single / not married	Rented apartment	0.002506	-8062	-92	-6446.0	-724	13	1	1	0	1	0	0	High skill tech staff	1	2	2	TUESDAY	7	0	0	0	1	1	0	Housing	0.352214	0.714284	0.651260	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	<NA>	1	0	1	0	-707.0	0	1	0	0	0	...	540000.0	0.00	0.0	0.0	0.0	0.0	-473.0	-106.0	-100.0	-106.0	5414423.0	0.0	0.0	0.0	-106	396369.000000	396369.00	380272.500000	380272.50	39640.5	NaN	NaN	0.0	0.0	0.0	0.0	0.0	10.0	-16096.5	0.104242	NaN	XAP	Approved	-707.0	Cash through the bank	XAP	New	POS	Audio/Video	XNA	low_normal	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	0.000000	-1406.0	NaN	1.0	0.0	0.0	0.0	1.0	1.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.000000	0.66 - 0.85	0.54 - 0.67	0.33 - 0.51	-289.0 - 365243.0	45000.0 - 270000.0	9 - 15	-3960000.0 - -558.0	-12412.0 - -7673.0	17820.0 - 3960000.0	-220213.42 - 0.0	-41875.0 - 120.0	0.0 - 0.5	0.0 - 2076.99	45000.0 - 238500.0	-762.0 - -279.0	0.0 - 0.25	-1728.0 - 0.0	16573.5 - 24916.5	112500.0 - 148500.0	1	-624.0 - -300.0	0.8 - 1.0	-153.0 - 0.0
99999	0	Cash loans	M	True	True	2	157500.0	746280.0	59094.0	675000.0	Unaccompanied	Working	Higher education	Married	House / apartment	0.020246	-13934	-1210	-3758.0	-5221	19	1	1	0	1	0	1	Drivers	4	3	3	WEDNESDAY	11	0	0	0	0	0	0	Self-employed	0.409607	0.366777	0.502878	0.0062	NaN	0.9886	NaN	NaN	NaN	0.2069	0.0417	NaN	NaN	NaN	NaN	NaN	NaN	0.0063	NaN	0.9886	NaN	NaN	NaN	0.2069	0.0417	NaN	NaN	NaN	NaN	NaN	NaN	0.0062	NaN	0.9886	NaN	NaN	NaN	0.2069	0.0417	NaN	NaN	NaN	NaN	NaN	NaN	NaN	block of flats	0.0162	Stone, brick	False	0	0	0	0	-2452.0	0	1	0	0	0	...	37800.0	0.00	0.0	0.0	0.0	0.0	-2708.0	-1073.0	-892.0	-1073.0	6671057.0	1.0	0.0	0.0	-552	48712.500000	194850.00	48766.500000	195066.00	4725.0	NaN	NaN	0.0	0.0	0.0	0.0	0.0	6.0	216.0	0.024223	NaN	XAP	Approved	-174.0	XNA	XAP	Repeater	POS	Auto Accessories	XNA	middle	3.0	0.0	1.0	0.0	0.0	0.0	0.0	4.0	0.0	4.0	0.0	0.0	1.250000	-2237.0	NaN	3.0	0.0	1.0	0.0	4.0	0.750000	0.000000	0.250000	0.000000	0.250000	0.0	0.0	0.0	1.0	0.0	0.0	0.0	3.0	0.0	0.0	0.000000	0.0 - 0.39	0.37 - 0.54	0.33 - 0.51	-1211.0 - -289.0	512338.5 - 808650.0	15 - 91	-558.0 - 16020.0	-15739.0 - -12412.0	0.0 - 6111.0	43926.19 - 141482.43	120.0 - 906.0	1.0 - 1.33	NaN	450000.0 - 679500.0	-4131.0 - -1571.0	0.0 - 0.25	-6551.0 - -4305.0	34587.0 - 230161.5	148500.0 - 202500.0	1	-2922.0 - -624.0	0.5 - 0.8	-718.0 - -347.0

100000 rows × 250 columns

Tight layout not applied. tight_layout cannot make axes width small enough to accommodate all axes decorations

set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.

set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
/tmp/ipykernel_17429/466980156.py in ?()
      1 importlib.reload(graph)
      2 
----> 3 graph.summary_df_features(features_matrix_sorted_imp)

~/data/projects/ppuodz-ML.4.1/shared/graph.py in ?(source_df)
   1207             plt.show()
   1208 
   1209         except Exception as ex:
   1210             plt.close(fig)
-> 1211             raise ex

~/data/projects/ppuodz-ML.4.1/shared/graph.py in ?(source_df)
   1207             plt.show()
   1208 
   1209         except Exception as ex:
   1210             plt.close(fig)
-> 1211             raise ex

~/miniconda3/envs/rapids_v2/lib/python3.10/site-packages/scipy/stats/_stats_py.py in ?(a, axis, nan_policy)
   1988     if contains_nan and nan_policy == 'omit':
   1989         a = ma.masked_invalid(a)
   1990         return mstats_basic.normaltest(a, axis)
   1991 
-> 1992     s, _ = skewtest(a, axis)
   1993     k, _ = kurtosistest(a, axis)
   1994     k2 = s*s + k*k
   1995 

~/miniconda3/envs/rapids_v2/lib/python3.10/site-packages/scipy/stats/_stats_py.py in ?(a, axis, nan_policy, alternative)
   1602 
   1603     if axis is None:
   1604         a = np.ravel(a)
   1605         axis = 0
-> 1606     b2 = skew(a, axis)
   1607     n = a.shape[axis]
   1608     if n < 8:
   1609         raise ValueError(

~/miniconda3/envs/rapids_v2/lib/python3.10/site-packages/scipy/stats/_axis_nan_policy.py in ?(***failed resolving arguments***)
    519                 # behavior of those would break backward compatibility.
    520 
    521                 if sentinel:
    522                     samples = _remove_sentinel(samples, paired, sentinel)
--> 523                 res = hypotest_fun_out(*samples, **kwds)
    524                 res = result_to_tuple(res)
    525                 res = _add_reduced_axes(res, reduced_axes, keepdims)
    526                 return tuple_to_result(*res)

~/miniconda3/envs/rapids_v2/lib/python3.10/site-packages/scipy/stats/_stats_py.py in ?(a, axis, bias, nan_policy)
   1190         a = ma.masked_invalid(a)
   1191         return mstats_basic.skew(a, axis, bias)
   1192 
   1193     mean = a.mean(axis, keepdims=True)
-> 1194     m2 = _moment(a, 2, axis, mean=mean)
   1195     m3 = _moment(a, 3, axis, mean=mean)
   1196     with np.errstate(all='ignore'):
   1197         zero = (m2 <= (np.finfo(m2.dtype).resolution * mean.squeeze(axis))**2)

~/miniconda3/envs/rapids_v2/lib/python3.10/site-packages/scipy/stats/_stats_py.py in ?(a, moment, axis, mean)
   1065             n_list.append(current_n)
   1066 
   1067         # Starting point for exponentiation by squares
   1068         mean = (a.mean(axis, keepdims=True) if mean is None
-> 1069                 else np.asarray(mean, dtype=dtype)[()])
   1070         a_zero_mean = a - mean
   1071 
   1072         eps = np.finfo(a_zero_mean.dtype).resolution * 10

TypeError: float() argument must be a string or a real number, not 'NAType'

Clustering

Clustering before using XGBoost can simplify data and possibly improve model performance by highlighting patterns that XGBoost may overlook. This preprocessing step reduces dimensionality and can enhance model interpretability, but its effectiveness depends on data relevance and feature importance evaluation.

K-Prototypes

Most suitable the dataset has clear boundaries and a roughly uniform distribution for optimal results. We've been unable to obtain clearly defined cluster when using it and based on the type of the dataset it's probably not the most suitable algorithm.

DBSCAN

Is an unsupervised algorithm which is more suitable for datasets with significant noise or irrelevant data points (e.g. data exhibits non-globular or irregularly shaped clusters)

Feature

• We've experimented

https://www.kaggle.com/c/home-credit-default-risk/data

AutoML - EvalML

Additionally, we've included a model selected using EvalML (an auto ML library) and a raw dataset (with Featuretools aggregations etc.) [TODO: include notebook]